This document walks through the code used to obtain the results of my project. The section include:

Data Cleaning Visualizations PCA and Hierarchical CLustering Model Building and Diagnostics

Loading Packages

The R commands for this project will be supplied as needed. First, the following libraries and functions will need to be loaded.

# rm(list = ls())
setwd("/Volumes/Macintosh HD - Data/SCHOOL/WGU/CapstoneProject")  ##set your path
library(tidycensus)
library(dplyr)
library(tidyverse)
library(tigris)
library(leaflet)
library(stringr)
library(sf)
library(purrr)
library(zipcode)
library(stringi)
library(ggplot2)
library(devtools)
library(tmap)         
library(tmaptools)  
library(FactoMineR)
library(tm)
library(stats)
library(openintro)
library(missMDA)
library(pscl)
library(factoextra)
library(openintro)
library(missMDA)
library(devtools)
library(PerformanceAnalytics)
library(ggrepel)
library(scales)
library(conflicted)
library(ResourceSelection)
library(caret)
library(MASS)
library(mpath)
library(DataExplorer)
library(corrplot)
library(knitr)

## set conflicts
conflict_prefer("select", "dplyr")
[conflicted] Removing existing preference
[conflicted] Will prefer dplyr::select over any other package
conflict_prefer("filter", "dplyr")
[conflicted] Removing existing preference
[conflicted] Will prefer dplyr::filter over any other package
## custom functions
medianWithoutNA = function(x) {
  median(x[which(!is.na(x))])
}
add_cols <- function(.data, ..., .f = sum){
  tmp <- dplyr::select_at(.data, dplyr::vars(...))
  purrr::pmap_dbl(tmp, .f = .f)
} ## great function to sum up multiple columns from https://github.com/tidyverse/dplyr/issues/4544

Data Cleaning

The farmers market, election, USDA, and zip code data files are uploaded. Because most of these files use state and county codes, such as “003,” we set the appropriate columns to character columns and add leading 0’s where necessary. Additionally we are adding the FIPS code to the USDA data, which is a concatonation of the sate and county codes.

The farmers market data can be found here: https://www.kaggle.com/madeleineferguson/farmers-markets-in-the-united-states?select=farmers_markets_from_usda.csv

The election data set (which is awesome btw) can be found at: https://public.opendatasoft.com/explore/dataset/usa-2016-presidential-election-by-county/information/?disjunctive.state&refine.state=Texas&dataChart=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%3D%3D&basemap=mapbox.light&location=5,38.73695,-100.08545

The USDA data was obtained using the QuickStat filters at: https://quickstats.nass.usda.gov/ Data was collected at the county level for te 2017 estimates.

farmers_markets = read.csv("Raw Data/farmers_markets_from_usda.csv") %>% mutate_if(is.factor, as.character) 

Election2016 = read.csv("Raw Data/Election2016byCounty.csv", 
                        colClasses = c(Fips = "character")) %>% mutate_if(is.factor, as.character) 

zips_in_county_subdiv = read.table("Raw Data/Zip Code Referential Table.txt", header = TRUE, sep = ",",
                         colClasses = c(ZCTA5 = "character", 
                         STATE =    "character",  COUNTY = "character", COUSUB = "character",  
                         GEOID  = "character", CLASSFP = "character"))
animal_total_sales = read.csv("Raw Data/USDA DATA/AnimalTotalByCounty.csv",
                        colClasses = c(State.ANSI = "character", County.ANSI = "character" )) %>%
  mutate(State.ANSI = ifelse(nchar(State.ANSI) == 1, paste0("0", State.ANSI), State.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 1, paste0("0", County.ANSI), County.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 2, paste0("0", County.ANSI), County.ANSI),
         Fips = paste0(State.ANSI,County.ANSI))

crop_total_sales = read.csv("Raw Data/USDA DATA/CropTotalsSalesDollars.csv",
          colClasses = c(State.ANSI = "character", County.ANSI = "character" )) %>%
  mutate(State.ANSI = ifelse(nchar(State.ANSI) == 1, paste0("0", State.ANSI), State.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 1, paste0("0", County.ANSI), County.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 2, paste0("0", County.ANSI), County.ANSI),
         Fips = paste0(State.ANSI,County.ANSI))

fruit_and_nuts_total_sales = read.csv("Raw Data/USDA DATA/FruitNutsSAlesDollars.csv",
          colClasses = c(State.ANSI = "character", County.ANSI = "character" )) %>%
  mutate(State.ANSI = ifelse(nchar(State.ANSI) == 1, paste0("0", State.ANSI), State.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 1, paste0("0", County.ANSI), County.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 2, paste0("0", County.ANSI), County.ANSI),
         Fips = paste0(State.ANSI,County.ANSI))

veggie_total_sales = read.csv("Raw Data/USDA DATA/VeggieTotalSalesDollars.csv",
          colClasses = c(State.ANSI = "character", County.ANSI = "character" )) %>%
  mutate(State.ANSI = ifelse(nchar(State.ANSI) == 1, paste0("0", State.ANSI), State.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 1, paste0("0", County.ANSI), County.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 2, paste0("0", County.ANSI), County.ANSI),
         Fips = paste0(State.ANSI,County.ANSI))

AG_land = read.csv("Raw Data/USDA DATA/AGLand.csv",
           colClasses = c(State.ANSI = "character", County.ANSI = "character" )) %>%
  mutate(State.ANSI = ifelse(nchar(State.ANSI) == 1, paste0("0", State.ANSI), State.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 1, paste0("0", County.ANSI), County.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 2, paste0("0", County.ANSI), County.ANSI),
         Fips = paste0(State.ANSI,County.ANSI))

The goal will be to create a frequency table with the farmers market data with the number of farmers markets per county, along with cleaning and matching incomplete entries. The USDA data can be combined into one data set. The zip code data will be used as a reference, assigning a given zip code to the county where the largest portion of residents live (zip codes can span more than one county). The election data set is complete. All of these tables will be joined by using the FIPS code as a primary key.

USDA Data Joining

FIPS codes will be used to join all six tables, which contain the values of the given statistic along with its CV. Alaska will not be included.

Next the six tables are joined together using the Fips code as a primary key. Alaska, state code 02, is being fitered out.

U1 = animal_total_sales %>% dplyr::select(Fips, State, County, State.ANSI,County.ANSI, Ag.District, Value, CV....) %>% rename( AnimalSales = Value, AnimalCV = CV....) %>% filter(State.ANSI != "02") %>% left_join(crop_total_sales %>% filter(Year == 2017) %>% select(Fips, Value, CV....)   %>% rename(CropSales = Value, CropCV = CV....), by = "Fips") %>%  left_join(fruit_and_nuts_total_sales %>% filter(Year == 2017) %>% select(Fips, Value, CV....)   %>% rename(FruitNutSales = Value, FruitNutCV = CV....), by = "Fips")%>%  left_join(veggie_total_sales %>% filter(Year == 2017) %>% select(Fips, Value, CV....)   %>% rename(VeggieSales = Value, VeggieCV = CV....), by = "Fips") %>% left_join(AG_land %>% filter(Year == 2017) %>% select(Fips, Value, CV....)   %>% rename(AgLandAcres = Value, AgLandCV = CV....), by = "Fips") %>% filter(!State == "02")

head(U1)
str(U1)
'data.frame':   3068 obs. of  16 variables:
 $ Fips         : chr  "01001" "01011" "01047" "01051" ...
 $ State        : chr  "ALABAMA" "ALABAMA" "ALABAMA" "ALABAMA" ...
 $ County       : chr  "AUTAUGA" "BULLOCK" "DALLAS" "ELMORE" ...
 $ State.ANSI   : chr  "01" "01" "01" "01" ...
 $ County.ANSI  : chr  "001" "011" "047" "051" ...
 $ Ag.District  : chr  "BLACK BELT" "BLACK BELT" "BLACK BELT" "BLACK BELT" ...
 $ AnimalSales  : chr  "8925000.00" " (D)" "36114000.00" "10212000.00" ...
 $ AnimalCV     : chr  "12.70" "(D)" "12.70" "12.70" ...
 $ CropSales    : chr  "12535000.00" " (D)" "27886000.00" "17382000.00" ...
 $ CropCV       : chr  "16.30" "(D)" "16.30" "16.30" ...
 $ FruitNutSales: chr  " (D)" "396,000" "204,000" "167,000" ...
 $ FruitNutCV   : chr  "(D)" "30.9" "30.9" "30.9" ...
 $ VeggieSales  : chr  "2,523,000" "121,000" "1,148,000" "379,000" ...
 $ VeggieCV     : chr  "32.8" "32.8" "32.8" "32.8" ...
 $ AgLandAcres  : chr  "13,211" "3,359" "38,177" "6,589" ...
 $ AgLandCV     : chr  "22.4" "22.4" "22.4" "22.4" ...
plot_missing(U1)

Using the NASS glossary, the code (D) is for disclosed. (H) refers to high CV value, over 99.95%, and (Z) refers to almost 0. This coding is causing numeric variables to be interpreted as a character. Let’s see what’s missing.

tot_na1 = apply(U1, 2, function(x) length(which(is.na(x))))
tot_na1[tot_na1 > 0]
    CropSales        CropCV FruitNutSales    FruitNutCV   VeggieSales      VeggieCV   AgLandAcres      AgLandCV 
            4             4           328           328           254           254            36            36 

The missing or disclosed numeric variables are removed and the columns are formatted to be numeric variables. Drop FruitNutSales, FruitNutCV, VeggieSales, and VeggieCV due to missingness.

## (D) = disclosed, (H) = high, 99.95%+, (Z) = almost 0 (from NASS glossary)
## remove commas, change to numeric variables
w = 7:16 ## column indices of variables to be imputed
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, trimws))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, function(y) gsub("(D)", NA, y)))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, function(y) gsub("(Z)", "0", y)))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, function(y) gsub("(H)", "99.95", y)))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, function(y) gsub(",", "", y)))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, as.numeric))
NAs introduced by coercionNAs introduced by coercionNAs introduced by coercionNAs introduced by coercion
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, as.numeric))

Missing and disclosed values will be imputed by using the agricultural district medians. Drop

U2 = U1 %>%  group_by(Ag.District) %>% filter(!is.na(State)) %>% 
             mutate(AnimalSales = ifelse(is.na(AnimalSales), medianWithoutNA(AnimalSales),
                                        AnimalSales),
             AnimalCV = ifelse(is.na(AnimalCV), medianWithoutNA(AnimalCV), AnimalCV),
             FruitNutSales  = ifelse(is.na(FruitNutSales ), medianWithoutNA(FruitNutSales ),
                                     FruitNutSales ),
             FruitNutCV  = ifelse(is.na(FruitNutCV), medianWithoutNA(FruitNutCV), FruitNutCV),
             VeggieSales  = ifelse(is.na(VeggieSales), medianWithoutNA(VeggieSales),
                                   VeggieSales),
            VeggieCV  = ifelse(is.na(VeggieCV), medianWithoutNA(VeggieCV), VeggieCV),
            CropSales = ifelse(is.na(CropSales), medianWithoutNA(CropSales), CropSales),
            CropCV  = ifelse(is.na(CropCV ), medianWithoutNA(CropCV ), CropCV ),
            AgLandAcres = ifelse(is.na(AgLandAcres), medianWithoutNA(AgLandAcres), AgLandAcres),
            AgLandCV = ifelse(is.na(AgLandCV), medianWithoutNA(AgLandCV), AgLandCV)
                   ) %>% ungroup()
tot_na2 = apply(U2, 2, function(x) length(which(is.na(x))))
tot_na2
         Fips         State        County    State.ANSI   County.ANSI   Ag.District   AnimalSales      AnimalCV 
            0             0             0             0             0             0             0             0 
    CropSales        CropCV FruitNutSales    FruitNutCV   VeggieSales      VeggieCV   AgLandAcres      AgLandCV 
            0             0            31            16            12            12             0             0 

The remaining NA’s will be imputed using state medians.

U3 = U2 %>%  group_by(State) %>%
  mutate(AnimalSales = ifelse(is.na(AnimalSales), medianWithoutNA(AnimalSales), AnimalSales),
         AnimalCV = ifelse(is.na(AnimalCV), medianWithoutNA(AnimalCV), AnimalCV),
        FruitNutSales  = ifelse(is.na(FruitNutSales ), medianWithoutNA(FruitNutSales ),      FruitNutSales ),
         FruitNutCV  = ifelse(is.na(FruitNutCV), medianWithoutNA(FruitNutCV), FruitNutCV),
         VeggieSales  = ifelse(is.na(VeggieSales), medianWithoutNA(VeggieSales), VeggieSales),
         VeggieCV  = ifelse(is.na(VeggieCV), medianWithoutNA(VeggieCV), VeggieCV),
         CropSales = ifelse(is.na(CropSales), medianWithoutNA(CropSales), CropSales),
         CropCV  = ifelse(is.na(CropCV ), medianWithoutNA(CropCV ), CropCV ),
          AgLandAcres = ifelse(is.na(AgLandAcres), medianWithoutNA(AgLandAcres), AgLandAcres),
          AgLandCV = ifelse(is.na(AgLandCV), medianWithoutNA(AgLandCV), AgLandCV)
   ) %>% ungroup()
tot_na3 = apply(U3, 2, function(x) length(which(is.na(x))))
tot_na3
         Fips         State        County    State.ANSI   County.ANSI   Ag.District   AnimalSales 
            0             0             0             0             0             0             0 
     AnimalCV     CropSales        CropCV FruitNutSales    FruitNutCV   VeggieSales      VeggieCV 
            0             0             0             0             0             0             0 
  AgLandAcres      AgLandCV 
            0             0 

Now we write the csv file to be used again later. It should be noted that this data set uses the current set of FIPS codes, which changed in 2015. There were a few changes to the codes, particularly in the state of Virginia.

USDA_data_cleaned = U3
write.csv(U3, "Cleaned Data/USDA_data.csv")

Election Data

This data set contains many useful pieces of information compiled through several sources. (https://github.com/Deleetdk/USA.county.data).

dim(Election2016)
[1] 3143  159
str(Election2016[ ,1:25])
'data.frame':   3143 obs. of  25 variables:
 $ State                        : chr  "Minnesota" "Kansas" "Oklahoma" "Montana" ...
 $ ST                           : chr  "MN" "KS" "OK" "MT" ...
 $ Fips                         : chr  "27017" "20127" "40107" "30085" ...
 $ County                       : chr  "Carlton County, Minnesota" "Morris County, Kansas" "Okfuskee County, Oklahoma" "Roosevelt County, Montana" ...
 $ Precincts                    : int  38 16 13 12 812 24 12 39 NA 12 ...
 $ Votes                        : int  18059 2568 3933 3502 320164 15177 11317 31094 NA 6367 ...
 $ Democrats.08..Votes.         : int  11501 907 1480 2564 207371 7127 2248 17940 NA 1913 ...
 $ Democrats.12..Votes.         : int  11389 718 1256 2086 193501 6921 1789 16330 NA 1733 ...
 $ Republicans.08..Votes.       : int  6549 1875 2643 1473 144262 7817 8658 12863 NA 4153 ...
 $ Republicans.12..Votes.       : int  6586 1773 2335 1514 133362 7973 8446 11996 NA 3911 ...
 $ Republicans.2016             : num  45.2 69.7 71 49.2 40.3 ...
 $ Democrats.2016               : num  46.8 22.8 24 42.9 54.4 ...
 $ Green.2016                   : num  1.29 2.34 NA 2.57 1.55 ...
 $ Libertarians.2016            : num  3.89 5.14 5.06 4.85 3.83 ...
 $ Republicans.2012             : num  35.7 69.2 65 41.2 40 ...
 $ Republicans.2008             : num  35.5 66 64.1 35.5 40.5 ...
 $ Democrats.2012               : num  61.8 28 35 56.8 58 ...
 $ Democrats.2008               : num  62.3 31.9 35.9 61.7 58.2 ...
 $ Less.Than.High.School.Diploma: num  9.7 9.9 21.2 10.9 11.6 24.8 30.4 12.5 35.9 23.2 ...
 $ At.Least.High.School.Diploma : num  90.3 90.1 78.8 89.1 88.4 75.2 69.6 87.5 64.1 76.8 ...
 $ At.Least.Bachelors.s.Degree  : num  21.4 16.6 10.9 17.3 34.8 13.4 11 18.5 9.6 12.1 ...
 $ Graduate.Degree              : num  7.2 7.7 3.1 4.7 15.1 4.8 3.2 6.2 0.5 4.9 ...
 $ School.Enrollment            : num  76.2 74.8 73.5 74.3 81.5 ...
 $ Median.Earnings.2010         : num  30427 25342 22072 27895 29747 ...
 $ White..Not.Latino..Population: num  89.5 94.2 64 36 74.2 ...
length(unique(Election2016$County))
[1] 3143

Some information is redundant. We will keep the percentages of votes by political party, and exclude the columns that contain the number of votes per party.

E1 = Election2016 %>%  filter(ST != "AK") %>% dplyr::select(State:Votes, Republicans.2016:Autumn.Tmin, temp, precip) 

Checking NA’s.

tot_na1 = apply(E1, 2, function(x) length(which(is.na(x))))
tot_na1[tot_na1 > 0]
                                              Precincts 
                                                      3 
                                                  Votes 
                                                      3 
                                       Republicans.2016 
                                                      3 
                                         Democrats.2016 
                                                      3 
                                             Green.2016 
                                                    513 
                                      Libertarians.2016 
                                                      3 
                                       Republicans.2012 
                                                      2 
                                       Republicans.2008 
                                                      2 
                                         Democrats.2012 
                                                      2 
                                         Democrats.2008 
                                                      2 
       Preschool.Enrollment.Ratio.enrolled.ages.3.and.4 
                                                      8 
Child.Poverty.living.in.families.below.the.poverty.line 
                                                      1 
                              Poor.physical.health.days 
                                                    333 
                                Poor.mental.health.days 
                                                    551 
                                        Low.birthweight 
                                                    215 
                                            Teen.births 
                                                     91 
                   Children.in.single.parent.households 
                                                      2 
                                          Adult.smoking 
                                                    429 
                        Sexually.transmitted.infections 
                                                    182 
                                    HIV.prevalence.rate 
                                                    803 
                                              Uninsured 
                                                      1 
                                           Unemployment 
                                                      1 
                                          Violent.crime 
                                                    168 
                                          Homicide.rate 
                                                   1868 
                                          Injury.deaths 
                                                    286 
                                       Infant.mortality 
                                                   1706 
                                                     CA 
                                                     42 
                                                      S 
                                                     10 
                                                    MAR 
                                                     10 
                                                    CFS 
                                                     10 
                                                   ACFS 
                                                     10 
                                               Mean.Alc 
                                                     10 
                                                Max.Alc 
                                                     10 
                                              Mixedness 
                                                     10 
                                              elevation 
                                                    227 
                                            Annual.Prcp 
                                                    511 
                                            Winter.Prcp 
                                                    511 
                                            Summer.Prcp 
                                                    511 
                                            Spring.Prcp 
                                                    511 
                                            Autumn.Prcp 
                                                    511 
                                            Annual.Tavg 
                                                   1392 
                                            Annual.Tmax 
                                                   1392 
                                            Annual.Tmin 
                                                   1392 
                                            Winter.Tavg 
                                                   1392 
                                            Winter.Tmax 
                                                   1392 
                                            Winter.Tmin 
                                                   1392 
                                            Summer.Tavg 
                                                   1392 
                                            Summer.Tmax 
                                                   1392 
                                            Summer.Tmin 
                                                   1392 
                                            Spring.Tavg 
                                                   1392 
                                            Spring.Tmax 
                                                   1392 
                                            Spring.Tmin 
                                                   1392 
                                            Autumn.Tavg 
                                                   1392 
                                            Autumn.Tmax 
                                                   1392 
                                            Autumn.Tmin 
                                                   1392 
                                                   temp 
                                                   1392 
                                                 precip 
                                                    511 

Homicide rates and infant mortality have too many NA’s so it will be dropped. Otherwise state medians will be used to impute the remaining missing values. There are many missing temperatures but these will be imputed by state medians, which should be fairly representative of the county.

E2 = E1 %>% dplyr::select(-Homicide.rate, -Infant.mortality ) %>%
  group_by(State) %>%
  mutate_each(funs(ifelse(is.na(.),median(., na.rm = TRUE),.)))  %>% ungroup()

Checking NA’s.

tot_na2 = apply(E2, 2, function(x) length(which(is.na(x))))
tot_na2[tot_na2 > 0]
         Green.2016 HIV.prevalence.rate                  CA         Annual.Prcp         Winter.Prcp 
                511                 119                   6                   5                   5 
        Summer.Prcp         Spring.Prcp         Autumn.Prcp         Annual.Tavg         Annual.Tmax 
                  5                   5                   5                   5                   5 
        Annual.Tmin         Winter.Tavg         Winter.Tmax         Winter.Tmin         Summer.Tavg 
                  5                   5                   5                   5                   5 
        Summer.Tmax         Summer.Tmin         Spring.Tavg         Spring.Tmax         Spring.Tmin 
                  5                   5                   5                   5                   5 
        Autumn.Tavg         Autumn.Tmax         Autumn.Tmin                temp              precip 
                  5                   5                   5                   5                   5 

Remaining missing will be imputed by global column medians.

E3 = E2  %>%
  mutate_each(funs(ifelse(is.na(.),median(., na.rm = TRUE),.))) 

Checking NA’s.

tot_na3 = apply(E3, 2, function(x) length(which(is.na(x))))
tot_na3[tot_na3 > 0]
named integer(0)

Good to go!

Election2016Cleaned = E3
write.csv(E3, "Cleaned Data/Election2016Cleaned.csv")

Census Data

Use tidycensus to link up with census API.

First load avaiable variables

census_variables = load_variables(year = 2010, dataset = "sf1", cache = TRUE)

I would like the following variables: family to household ratio = P018002/P018001 married household to family household ratio = P018003/P018002 urban ratio = H002002 / H002001 renter occupied = H004004 / H004001 household size categories = H013002:H013008 / H013001 Ave househould size = H012001 Ave family size = P037A001 total female pop = P012026/ P012001 male age groups = (P012003:7 (under 20)), (P012008:10 (20-24)) , (P012011:14( 25-44)), (P012015:6 (45-54)), (P012017 (55-59)), (P012018:25 (60+)) / P012002 (total) female age groups = (P0120273:31 (under 20)), (P012032:34 (20-24)) , (P012038:38( 25-44)), (P012039:40 (45-54)), (P012041 (55-59)), (P012042:49 (60+)) / P012026 (total) husband - wife - children families ratio = P038003/ P038001

vars = c(paste0("P01800",1:3), "H002001", "H002002", "H004001" , "H004004", "H012001",
         paste0("H01300", 1:8), "P037A001", paste0("P01200", 1:9), paste0("P0120", 10:49),
         "P038001", "P038003")

Now let’s turn this raw data into the ratios I’m after and drop the raw numbers. These are the first of my derived variables.

Census_data_cleaned = census_raw_data %>% rename(Fips = GEOID) %>% 
                          mutate(FamilyRatio = P018002/P018001,
                                 MarriedHouseholdRatio = P018003/P018002,
                                 RenterOccupied = H004004/H004001,
                                 AveHousehouldSize =    H012001,
                                 AveFamilySize  = P037A001,
                                 TotalFemale  = P012026/ P012001,
                                 MaleUnder20 =  add_cols(.,  P012003:P012007)  / P012002,
                                 Male20to24 = add_cols(.,P012008:P012010) / P012002,
                                 Male25to44 = add_cols(.,P012011:P012014) / P012002,
                                 Male44to54 = add_cols(.,P012015:P012016) / P012002,
                                 Male55to59 = P012017 / P012002,
                                 MaleOver59 = add_cols(.,P012018:P012025) / P012026,
                                 FemaleUnder20 = add_cols(.,P012027:P012031) / P012026,
                                 Female20to24 = add_cols(.,P012032:P012034) / P012026,
                                 Female25to44 = add_cols(.,P012035:P012038) / P012026,
                                 Female44to54 = add_cols(.,P012039:P012040) / P012026,
                                 Female55to59 = P012041 / P012026,
                                 FemaleOver59 = add_cols(.,P012042:P012049) / P012026,
                                 HusbandWifeFamilyRatio = P038003/  P038001
    
                                 ) %>% dplyr::select(-all_of(vars))

Just like a mirror -looking good!

def’s for the census data https://www.census.gov/programs-surveys/cps/technical-documentation/subject-definitions.html#household

Zip Code Referential Table

The farmers market table contains some incomplete records. The county of each farmers market is needed but missing or unmatched on the farmers market table. Additionally, there is no FIPS code provided on the table. The purpose of this section have a reference so a zip code can be matched to its county. However, some zip code span more than one county. To resolve this, it has been decided to match each zip code with the county where the most amount of residents from that zip code.

A copy of a zip code to county subdivision reference table is available through the US Census Bureau.

str(zips_in_county_subdiv)
'data.frame':   107996 obs. of  26 variables:
 $ ZCTA5        : chr  "00601" "00601" "00601" "00601" ...
 $ STATE        : chr  "72" "72" "72" "72" ...
 $ COUNTY       : chr  "001" "001" "001" "001" ...
 $ COUSUB       : chr  "00401" "13645" "30458" "32049" ...
 $ CLASSFP      : chr  "Z1" "Z1" "Z1" "Z1" ...
 $ GEOID        : chr  "7200100401" "7200113645" "7200130458" "7200132049" ...
 $ POPPT        : int  4406 1038 1337 140 254 1176 865 276 504 606 ...
 $ HUPT         : int  1968 425 509 60 115 461 347 128 187 252 ...
 $ AREAPT       : num  1942319 9420707 16497991 7312819 2763743 ...
 $ AREALANDPT   : num  1942319 9387179 16271520 6974412 2763743 ...
 $ ZPOP         : int  18570 18570 18570 18570 18570 18570 18570 18570 18570 18570 ...
 $ ZHU          : int  7744 7744 7744 7744 7744 7744 7744 7744 7744 7744 ...
 $ ZAREA        : num  1.67e+08 1.67e+08 1.67e+08 1.67e+08 1.67e+08 ...
 $ ZAREALAND    : num  1.67e+08 1.67e+08 1.67e+08 1.67e+08 1.67e+08 ...
 $ CSPOP        : int  4406 1054 1337 140 853 1176 865 276 553 606 ...
 $ CSHU         : int  1968 434 509 60 369 461 347 128 207 252 ...
 $ CSAREA       : num  1942319 9494851 16497991 7312952 7695788 ...
 $ CSAREALAND   : num  1942319 9461323 16271520 6974412 7443424 ...
 $ ZPOPPCT      : num  23.73 5.59 7.2 0.75 1.37 ...
 $ ZHUPCT       : num  25.41 5.49 6.57 0.77 1.49 ...
 $ ZAREAPCT     : num  1.16 5.63 9.85 4.37 1.65 ...
 $ ZAREALANDPCT : num  1.17 5.63 9.76 4.18 1.66 ...
 $ CSPOPPCT     : num  100 98.5 100 100 29.8 ...
 $ CSHUPCT      : num  100 97.9 100 100 31.2 ...
 $ CSAREAPCT    : num  100 99.2 100 100 35.9 ...
 $ CSAREALANDPCT: num  100 99.2 100 100 37.1 ...

This table contains data on the population, number of housing units, land area for section of the zip code contained in each county. The “CS” prefix refers to the county sibdivision of that zip code. A county is composed of multiple subdivisions, and we can use that fact to calculate the total land area and housing units of each county given this table.

Z1 = zips_in_county_subdiv %>% group_by(STATE, COUNTY) %>% 
  mutate(CountyHU = sum(CSHU), CountyArea = sum(CSAREA)) %>% ungroup()

The zip sections are arranged by descending population. FIPS codes are added and only the most popluous section of each unique zip code is selected.

Z2 = Z1 %>% arrange(ZCTA5, desc(POPPT)) %>% mutate(Fips = paste0(STATE, COUNTY))  %>% select(-CLASSFP)##sort by zip and population
zips = unique(zips_in_county_subdiv$ZCTA5) ## keep only the first unique zip
Z3 = Z2[!duplicated(Z2$ZCTA5),  ] #clean

The tidyCENSUS package contians the county names corresponding to each FIPS code. We create a reference table.

County_Code_Ref = fips_codes %>% mutate(Fips = paste0(state_code, county_code))
head(County_Code_Ref)

Lastly we join the Z3 table with the County_Code_Ref table to include county names.

Z4 = left_join(Z3, County_Code_Ref %>% dplyr::select(Fips, county, state_name, state), by = "Fips") %>% rename(ST = state, Zip = ZCTA5)

Lastly rearrange to be more user-friendly.

zip_unique = Z4 %>% dplyr::select(Zip, Fips, county, state_name, ST, STATE:CountyArea)
write.csv(zip_unique, "Cleaned Data/Zip Unique.csv")

Farmers Market Data Set Cleaning

A frequency of the number of farmers market per county will be created. Missing or incomplete county names will need to be imputed by zip code. If a zip code is missing, the zipcode package can match cities to zip codes, although some cities span more than one zip code.

dim(farmers_markets)
[1] 8804   59
str(farmers_markets[ ,1:30])
'data.frame':   8804 obs. of  30 variables:
 $ FMID       : int  1018261 1018318 1009364 1010691 1002454 1011100 1009845 1005586 1008071 1012710 ...
 $ MarketName : chr  " Caledonia Farmers Market Association - Danville" " Stearns Homestead Farmers' Market" "106 S. Main Street Farmers Market" "10th Steet Community Farmers Market" ...
 $ Website    : chr  "https://sites.google.com/site/caledoniafarmersmarket/" "http://www.StearnsHomestead.com" "http://thetownofsixmile.wordpress.com/" "" ...
 $ Facebook   : chr  "https://www.facebook.com/Danville.VT.Farmers.Market/" "StearnsHomesteadFarmersMarket" "" "" ...
 $ Twitter    : chr  "" "" "" "" ...
 $ Youtube    : chr  "" "" "" "" ...
 $ OtherMedia : chr  "" "" "" "http://agrimissouri.com/mo-grown/grodetail.php?type=mo-grown&ID=275" ...
 $ street     : chr  "" "6975 Ridge Road" "106 S. Main Street" "10th Street and Poplar" ...
 $ city       : chr  "Danville" "Parma " "Six Mile" "Lamar " ...
 $ County     : chr  "Caledonia" "Cuyahoga" "Pickens" "Barton" ...
 $ State      : chr  "Vermont" "Ohio" "South Carolina" "Missouri" ...
 $ zip        : chr  "5828" "" "29682" "64759" ...
 $ Season1Date: chr  "06/14/2017 to 08/30/2017" "06/24/2017 to 09/30/2017" "" "04/02/2014 to 11/30/2014" ...
 $ Season1Time: chr  "Wed: 9:00 AM-1:00 PM;" "Sat: 9:00 AM-1:00 PM;" "" "Wed: 3:00 PM-6:00 PM;Sat: 8:00 AM-1:00 PM;" ...
 $ Season2Date: chr  "09/06/2017 to 10/18/2017" "" "" "" ...
 $ Season2Time: chr  "Wed: 2:00 PM-6:00 PM;" "" "" "" ...
 $ Season3Date: chr  "" "" "" "" ...
 $ Season3Time: chr  "" "" "" "" ...
 $ Season4Date: chr  "" "" "" "" ...
 $ Season4Time: chr  "" "" "" "" ...
 $ x          : num  -72.1 -81.7 -82.8 -94.3 -73.9 ...
 $ y          : num  44.4 41.4 34.8 37.5 40.8 ...
 $ Location   : chr  "" "" "" "" ...
 $ Credit     : chr  "Y" "Y" "Y" "Y" ...
 $ WIC        : chr  "Y" "N" "N" "N" ...
 $ WICcash    : chr  "N" "N" "N" "N" ...
 $ SFMNP      : chr  "Y" "Y" "N" "N" ...
 $ SNAP       : chr  "N" "N" "N" "N" ...
 $ Organic    : chr  "Y" "-" "-" "-" ...
 $ Bakedgoods : chr  "Y" "Y" "" "Y" ...

Inspecting the zip codes.

table(nchar(farmers_markets$zip))

   0    1    2    3    4    5    6   10 
 947    1   10   41  843 6950    2   10 
sum(is.na(farmers_markets$zip))
[1] 0

Some zip code are missing leading 0’s, some have a hyphenated suffix, some have mistakes, and some are missing. We add an ID column and add leading 0’s to 3 and 4 digit zips. Additionally, we need the county names to match up correctly in order to locate the FIPS code. So we trim whitespace, fix capitalization, and add Parish to county names in Louisiana.

farmers_markets = cbind( ID = 1:nrow(farmers_markets), farmers_markets)
F1 = farmers_markets %>% mutate(City = str_to_title(trimws(city, which = "both")),
                                State = str_to_title(trimws(State)),
                                County = str_to_title(County),
                                County = str_replace(County, "County",""),
                                County = trimws(County, which = "both"),
                                ST = state2abbr(State),
                                zip = ifelse(nchar(zip) == 3, paste0("0",zip), zip),
                                zip = ifelse(nchar(zip) == 4, paste0("0",zip), zip),
                                zip = ifelse(nchar(zip) == 10, str_sub(zip, 1, 5), zip),
                                County = ifelse(State == "LA", paste0(County, "Parish"),
                                                paste(County, "County", sep =" "))) %>%
                          select(ID, City, County, State, ST, zip)

Next we use the zipcode package to find zip codes using city and state names if a remainig zip code does not have five digits.

data("zipcode")
F2 = F1 %>% mutate(zip = ifelse(nchar(zip == 5), zip,
                               zipcode$zip[zipcode$city == City & zipcode$state == ST] ),
                   County = ifelse(ST == "DC", str_replace(County, "County", ""), County))
sum(is.na(F2$County))
[1] 42

There are stll a few missing counties, which we will deal with later.

F3 = left_join(F2, County_Code_Ref, by = c(c("County"="county"), c("ST" = "state")), all.x=T) %>%
        mutate(Fips = ifelse(ST == "DC", "11001", Fips)) %>%
        select(-state_name)

Now we drop our first observations outside the lower 49 states and find out how many FIPS codes are still missing.

dropped_IDs = F3$ID[F3$State %in% c("Alaska", "Puerto Rico", "Virgin Islands")] ## create running vector of dropped ID's
F4 = F3 %>% filter(!State %in% c("Alaska", "Puerto Rico", "Virgin Islands"))
sum(is.na(F4$Fips))
[1] 326

We did drop 83 farmers markets, but have 326 FIPS codes to find. We put these observations into a new data frame. Let’s see if there zip codes seem reliable.

na_county = F4[is.na(F4$Fips), c(1,2,3,6)]
table(nchar(na_county$zip))

  0   2   5 
 44   2 280 

Most of these missing do have zip codes so let’s see if we can find the counties using the zip code reference table.

found_county = left_join(na_county, zip_unique, by = c("zip" = "Zip" ) ) %>% select(ID:ST)

These found counties will be joined to the running farmers market table.

F5 = left_join(F4, found_county, by = "ID")
F6 = F5 %>% mutate(County = ifelse(is.na(County.x), County.y, County.x),
                   Fips = ifelse(is.na(Fips.x), Fips.y, Fips.x)) %>%
            select(ID, City.x, County, State, ST.x, zip.x, state_code, county_code, Fips )
colnames(F6) = gsub(".x", "", colnames(F6))
sum(is.na(F6$Fips))
[1] 52

Now we’re left with 52 Fips to find.

As many of the last few remaining NA’s are located.

na_county2 = F6[is.na(F6$Fips), ] %>% 
                mutate(County = str_replace(County, "County", ""),
                       County = trimws(County, which = "both"),
                       County = ifelse(ST == "LA", paste(County, "Parish", sep = " "),
                                       paste(County, "County", sep =" ")),
                       County = str_replace(County, "St.", "Saint"),
                       County = ifelse(str_sub(County, 1, 2) %in% c("De", "Mc", "O'", "Du"),
                                       paste0(str_sub(County, 1,2), str_to_upper(str_sub(County, 3,3)), str_sub(County, 4,-1)),  County),
                       County = ifelse(City == "Colorado Springs", "El Paso County", County),
                       County = ifelse(City == "St. Louis", "St. Louis County", County),
                       County = ifelse(County == "Fond Du Lac County", "Fond du Lac County", County)
                       )
found_county2 = left_join(na_county2, County_Code_Ref, by = c(c("County" = "county"), c("ST" = "state")))                       

Join these last counties that were able to be matched to the running farmers market table, and drop the remaining.

dropped_IDs = c(dropped_IDs,found_county2$ID[is.na(found_county2$Fips.y)])

found_county2 = found_county2[!is.na(found_county2$Fips.y),] %>%
                    select(ID, City, County, State, ST, zip, 
                           Fips.y, state_code.y, county_code.y)
## merge with running farmers market table
F7 = left_join(F6, found_county2, by = "ID") %>%
        mutate( Fips = ifelse(is.na(Fips), Fips.y, Fips),
                County = ifelse(is.na(Fips), County.y, County.x)) %>%
        select(ID, City.x, County.x, State.x, ST.x, zip.x, state_code, 
               county_code, Fips) 
colnames(F7) = gsub(".x","", colnames((F7)))

We create a CSV file containing each farmers market’s FIPS code.

length(dropped_IDs) ## only had to drop 116 of 8,804! (some are mobile markets)
[1] 116
write.csv(F7, "Cleaned Data/FarmersMarketEach.csv")  ## each market
Droppped_FM = farmers_markets[dropped_IDs, ] ##data frame of dropped markets

Of our 8,804 original farmers markets, we dropped 116 which were either outside the lower 49 states or we were unable to match the market to one county. It should be noted that some of these markets are “mobile markets.”

Next we write a csv file with each market. This is not the frequency table we’re after, but this is a useful table for later applications.

## lastly make a freq table by each Fips. Only lower 49 states
Fips_table = data.frame(table(F7$Fips)) %>% rename( Fips = Var1) %>% mutate_if(is.factor, as.character) 
F8 = left_join(County_Code_Ref %>% filter(!state %in% c("AK","PR","VI")), 
               Fips_table, by = "Fips", all.x = TRUE) %>% 
          mutate(Freq = ifelse(is.na(Freq), 0, Freq))
AllCountyFMfreq = F8
write.csv(F8, "Cleaned Data/AllCountyFarmersMarketFreq.csv")

Final Join

The last step now is to join the Election, USDA, Zip Code, and Farmers Market frequency tables together.

Below we join the election and USDA data to the farmer market frequency table, and inspect any NA’s.

V1 = full_join(AllCountyFMfreq %>% dplyr::select(Fips,county, state, state_name, Freq), USDA_data_cleaned, by = "Fips") 
V12 = full_join(V1, Election2016Cleaned, by = "Fips") %>% dplyr::select(-State.x, -County.x, -State.ANSI, -County.ANSI)
V2 = full_join(V12, Census_data_cleaned, by ="Fips")

tot_naF = apply(V2, 2, function(x) length(which(is.na(x))))
tot_naF[tot_naF > 0 ]
                                               Ag.District 
                                                        58 
                                               AnimalSales 
                                                        58 
                                                  AnimalCV 
                                                        58 
                                                 CropSales 
                                                        58 
                                                    CropCV 
                                                        58 
                                             FruitNutSales 
                                                        58 
                                                FruitNutCV 
                                                        58 
                                               VeggieSales 
                                                        58 
                                                  VeggieCV 
                                                        58 
                                               AgLandAcres 
                                                        58 
                                                  AgLandCV 
                                                        58 
                                                   State.y 
                                                        12 
                                                        ST 
                                                        12 
                                                  County.y 
                                                        12 
                                                 Precincts 
                                                        12 
                                                     Votes 
                                                        12 
                                          Republicans.2016 
                                                        12 
                                            Democrats.2016 
                                                        12 
                                                Green.2016 
                                                        12 
                                         Libertarians.2016 
                                                        12 
                                          Republicans.2012 
                                                        12 
                                          Republicans.2008 
                                                        12 
                                            Democrats.2012 
                                                        12 
                                            Democrats.2008 
                                                        12 
                             Less.Than.High.School.Diploma 
                                                        12 
                              At.Least.High.School.Diploma 
                                                        12 
                               At.Least.Bachelors.s.Degree 
                                                        12 
                                           Graduate.Degree 
                                                        12 
                                         School.Enrollment 
                                                        12 
                                      Median.Earnings.2010 
                                                        12 
                             White..Not.Latino..Population 
                                                        12 
                               African.American.Population 
                                                        12 
                                Native.American.Population 
                                                        12 
                                 Asian.American.Population 
                                                        12 
                                       Other.Race.or.Races 
                                                        12 
                                         Latino.Population 
                                                        12 
                        Children.Under.6.Living.in.Poverty 
                                                        12 
                     Adults.65.and.Older.Living.in.Poverty 
                                                        12 
                                          Total.Population 
                                                        12 
          Preschool.Enrollment.Ratio.enrolled.ages.3.and.4 
                                                        12 
              Poverty.Rate.below.federal.poverty.threshold 
                                                        12 
                                          Gini.Coefficient 
                                                        12 
   Child.Poverty.living.in.families.below.the.poverty.line 
                                                        12 
           Management.professional.and.related.occupations 
                                                        12 
                                       Service.occupations 
                                                        12 
                              Sales.and.office.occupations 
                                                        12 
                  Farming.fishing.and.forestry.occupations 
                                                        12 
Construction.extraction.maintenance.and.repair.occupations 
                                                        12 
 Production.transportation.and.material.moving.occupations 
                                                        12 
                                                     White 
                                                        12 
                                                     Black 
                                                        12 
                                                  Hispanic 
                                                        12 
                                                     Asian 
                                                        12 
                                                Amerindian 
                                                        12 
                                                     Other 
                                                        12 
                                              White..Asian 
                                                        12 
                                          Sire.Homogeneity 
                                                        12 
                                                Median.Age 
                                                        12 
                                                       lon 
                                                        12 
                                                       lat 
                                                        12 
                                 Poor.physical.health.days 
                                                        12 
                                   Poor.mental.health.days 
                                                        12 
                                           Low.birthweight 
                                                        12 
                                               Teen.births 
                                                        12 
                      Children.in.single.parent.households 
                                                        12 
                                             Adult.smoking 
                                                        12 
                                             Adult.obesity 
                                                        12 
                                                  Diabetes 
                                                        12 
                           Sexually.transmitted.infections 
                                                        12 
                                       HIV.prevalence.rate 
                                                        12 
                                                 Uninsured 
                                                        12 
                                              Unemployment 
                                                        12 
                                             Violent.crime 
                                                        12 
                                             Injury.deaths 
                                                        12 
                                                        CA 
                                                        12 
                                                         S 
                                                        12 
                                                       MAR 
                                                        12 
                                                       CFS 
                                                        12 
                                                      ACFS 
                                                        12 
                                                  Mean.Alc 
                                                        12 
                                                   Max.Alc 
                                                        12 
                                                 Mixedness 
                                                        12 
                                                 elevation 
                                                        12 
                                               Annual.Prcp 
                                                        12 
                                               Winter.Prcp 
                                                        12 
                                               Summer.Prcp 
                                                        12 
                                               Spring.Prcp 
                                                        12 
                                               Autumn.Prcp 
                                                        12 
                                               Annual.Tavg 
                                                        12 
                                               Annual.Tmax 
                                                        12 
                                               Annual.Tmin 
                                                        12 
                                               Winter.Tavg 
                                                        12 
                                               Winter.Tmax 
                                                        12 
                                               Winter.Tmin 
                                                        12 
                                               Summer.Tavg 
                                                        12 
                                               Summer.Tmax 
                                                        12 
                                               Summer.Tmin 
                                                        12 
                                               Spring.Tavg 
                                                        12 
                                               Spring.Tmax 
                                                        12 
                                               Spring.Tmin 
                                                        12 
                                               Autumn.Tavg 
                                                        12 
                                               Autumn.Tmax 
                                                        12 
                                               Autumn.Tmin 
                                                        12 
                                                      temp 
                                                        12 
                                                    precip 
                                                        12 
                                                      NAME 
                                                        12 
                                               FamilyRatio 
                                                        12 
                                     MarriedHouseholdRatio 
                                                        12 
                                            RenterOccupied 
                                                        12 
                                         AveHousehouldSize 
                                                        12 
                                             AveFamilySize 
                                                        12 
                                               TotalFemale 
                                                        12 
                                               MaleUnder20 
                                                        12 
                                                Male20to24 
                                                        12 
                                                Male25to44 
                                                        12 
                                                Male44to54 
                                                        12 
                                                Male55to59 
                                                        12 
                                                MaleOver59 
                                                        12 
                                             FemaleUnder20 
                                                        12 
                                              Female20to24 
                                                        12 
                                              Female25to44 
                                                        12 
                                              Female44to54 
                                                        12 
                                              Female55to59 
                                                        12 
                                              FemaleOver59 
                                                        12 
                                    HusbandWifeFamilyRatio 
                                                        12 

Here is a view of our last few NA’s.

final_nas1 = V2[is.na(V2$Ag.District), 1:16]
final_nas2 = V2[is.na(V2$MAR), 1:16]
final_nas3 = rbind(final_nas1,final_nas2)
final_nas4 = final_nas3[!duplicated(final_nas3$Fips), ]
final_nas4

The 12 rows missing election data and census data, which all have 0 farmers market frequency, are dropped.

## drop rows with both missing Election2016 data from running final table
V3 = V2 %>% filter( !is.na(MAR))  ## all row is missing and no Freq's lost
dropped_fips = setdiff(V2$Fips, V3$Fips)

The county area and housing unit from the zips table needs to be added.

V4 = left_join(V3, zip_unique %>% dplyr::select(Fips, CountyArea, CountyHU), by = "Fips") %>% distinct() %>% dplyr::select( -state, -County.y, -State.y)

Last use state medians to impute missing USDA data. The state of Virginia has multiple entries imputed.

V5 = V4 %>% group_by(state_name) %>%
  mutate(CountyArea = ifelse(is.na(CountyArea), medianWithoutNA(CountyArea), CountyArea),
         CountyHU = ifelse(is.na(CountyHU), medianWithoutNA(CountyHU), CountyHU),
         AnimalSales = ifelse(is.na(AnimalSales), medianWithoutNA(AnimalSales), AnimalSales),
         AnimalCV = ifelse(is.na(AnimalCV), medianWithoutNA(AnimalCV), AnimalCV),
         FruitNutSales  = ifelse(is.na(FruitNutSales ), medianWithoutNA(FruitNutSales ), FruitNutSales ),
         FruitNutCV  = ifelse(is.na(FruitNutCV), medianWithoutNA(FruitNutCV), FruitNutCV),
         VeggieSales  = ifelse(is.na(VeggieSales), medianWithoutNA(VeggieSales), VeggieSales),
         VeggieCV  = ifelse(is.na(VeggieCV), medianWithoutNA(VeggieCV), VeggieCV),
         CropSales = ifelse(is.na(CropSales), medianWithoutNA(CropSales), CropSales),
         CropCV  = ifelse(is.na(CropCV ), medianWithoutNA(CropCV ), CropCV ),
         AgLandAcres = ifelse(is.na(AgLandAcres), medianWithoutNA(AgLandAcres), AgLandAcres),
        AgLandCV = ifelse(is.na(AgLandCV), medianWithoutNA(AgLandCV), AgLandCV)

  ) %>% ungroup() 

Let’s see what is still missing.

Since agriculture is likely limited in DC, it will be assigned the minimum value for each missing agricultural variable.

V5[291,6:15] = as.list(apply(V5[ ,6:15],2, function(x) min(x, na.rm = TRUE)))

Last remaining NA’s.

V5[is.na(V5$Ag.District),1:8 ]

Many Virginia cities are missing the agricultural district name, so those missing will get their own category. Otherwise the rest will be in an “unknown” category.

V5[is.na(V5$Ag.District) & V5$ST == "VA", 5] = "Virginia (Unknown)"
V5[is.na(V5$Ag.District), 5] = "Unknown"
V5 = data.frame(V5)

Let’s check to see if all NA’s are gone and the frequencies add up correctly.

tot_na5 = apply(V5, 2, function(x) length(which(is.na(x))))
tot_na5[tot_na5 > 0 ]
named integer(0)
sum(V5$Freq) ## adds up correctly! 8804 original with 116 dropped
[1] 8688
8804 - 116
[1] 8688

We add row names for presentation by concatenating county nd state names. Then we rename and reorder the columns.

rn = make.names( paste(V5$county, V5$ST, sep = " "), unique = TRUE)
V55 = V5 %>% mutate(RowName = rn) %>% rename(County = county, State = state_name)

V6 = V55 %>% dplyr::select(Fips, RowName, County, State, Ag.District, Freq, CountyArea, CountyHU, AnimalSales:CountyHU)

Let’s make some derived variable looking at the change in votes from 2008 and 2012 for both politcal parties. Also inlude the range from min amd max temeratures as a measure of temperature variability. Additionally, some of the race and weather variables are redundant, so they will be dropped.

V66 = V6 %>% mutate(
                 DemChange08 = 100*(Democrats.2016 - Democrats.2008)/Democrats.2008,
                 DemChange12 = 100*(Democrats.2016 - Democrats.2012)/Democrats.2012,
                 RepChange08 = 100*(Republicans.2016 - Republicans.2008)/Republicans.2012,
                 RepChange12 = 100*(Republicans.2016 - Republicans.2012)/Republicans.2008,
                 OtherRace = Other,
                 TempRange = Annual.Tmax - Annual.Tmin) %>% dplyr::select(-Asian.American.Population,  
                 -Native.American.Population, -Annual.Prcp, -White..Not.Latino..Population, -Latino.Population,
                 -African.American.Population, -Other.Race.or.Races, -Other  )

And we are done!

All_Final_Data_Cleaned = V66 %>% dplyr::select(-ST, -NAME)
rownames(All_Final_Data_Cleaned) = All_Final_Data_Cleaned$RowName
write.csv(All_Final_Data_Cleaned, "Cleaned Data/All_Final_Data_Cleaned.csv")
head(All_Final_Data_Cleaned)

Our final data set contains 3,114 rows (counties) of 126 variables.

Derived Variables:
DemChange08, DemChange12, RepChange08, RepChange12, TempRange, FamilyRatio, MarriedHouseholdRatio, RenterOccupied, AveHousehouldSize, AveFamilySize, TotalFemale, MaleUnder20, Male20to24, Male25to44, Male44to54, Male55to59, MaleOver59, FemaleUnder20, Female20to24, Female25to44, Female44to54, Female55to59, FemaleOver59, HusbandWifeFamilyRatio

Droppped rows IDs from original farmers markets data set.

dropped_IDs
  [1]  196  197  217 1158 1183 1313 1605 1618 1885 1917 2290 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304
 [25] 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2435 2476 2564
 [49] 2696 3330 3593 3695 3730 3972 4019 4044 4057 4058 4179 4925 4926 4927 5236 5240 5269 5286 5296 5333 5569 6595 6876 7009
 [73] 7050 7070 7071 7182 7474 7484 7675 7849 7918 8272 8598 1157 1916 2038 2533 2538 2541 2598 2690 2767 2891 2942 3084 3117
 [97] 3226 3261 3703 4720 4930 5074 5787 6334 6430 6464 6484 6728 7068 7312 7480 7801 7859 8014 8513 8766

Data Visualization and Exploration

Let’s start by making some aliases to facilitate the coding.

## alias for master tables
D1 = All_Final_Data_Cleaned
D2 = farmers_markets
D3 = FarmersMarketEach

Use tidycensus to upload county geography using the API (requires internet connection and may take a few minutes). I also had geo.shapes file on the election metadata. I think there is a difference in FIPS codes since the 2015 switch between the two files, so I’ll probably end up using the geo variable.

Here is some basic info.

## Pie chart
B1 = data.frame(table(D1$Freq)) %>% rename( Markets = Var1) ## freq tables of # of markets
B2 = B1 %>% mutate(Y = ifelse(B1$Markets != "0" ,1,0)) ## make an "at least one" category
X3 = c("Zero", "At least One") ## name it
Y3 =c()
Y3[1] = sum(B2$Freq[B2$Y == 0]) ## total 0's
Y3[2] = sum(B2$Freq[B2$Y == 1]) ## total "at least one"
B3 = data.frame(X3, Y3 ) %>% rename( Freq = Y3)

pie <- ggplot(B3, aes(x="", y=Freq, fill=X3))+
  geom_bar(width = 1, stat = "identity") + coord_polar("y")
PieChart = pie + theme_minimal() + ggtitle( "US Farmers Markets Per County") +
  theme(plot.title = element_text( face = "bold")) + 
  ylab("n = 3,114 counties") + xlab("") +
  labs(fill = "") + geom_text(aes(y = Freq/2 + c(0, cumsum(Freq)[-length(Freq)]),
                                  label = paste(round((Freq/sum(B3[ ,2]))*100,2), "%",sep="")), size=3) + 
  scale_fill_manual(values = c("khaki2", "olivedrab4"))
  
PieChart

Nearly 3/4 of counties have at least one farmers market.

ggplot(All_Final_Data_Cleaned, aes(x=Freq, )) + geom_histogram( fill = "olivedrab4", binwidth = 1) + labs( x= "Number of Farmers Market per County", y ="Frequency", title = "Distribution of Response Variable") + theme(plot.title = element_text(face = "bold", size = 14))

The distribution of the number of farmers markets per county is skewed right. Most counties have 0-2 farmers markets. There are some outliers, like Los Angeles County with 128 farmers markets (and about 10 million residents).

Since the response variable is a count variable, it is likely to follow either a Poisson or negative binomial distribution, if there is no overdispersion present. A zero inflated model should be investigated, as there may be factors present in some counties that prevent the county from having any farmers markets.

Next let’s see the distribution of markets by state.

B3 = data.frame(table(FarmersMarketEach$State)) %>% rename( State = Var1) %>% 
        arrange(Freq) %>% filter(State != "Virgin Islands")
BarchartStates = B3  %>%
                      ggplot(aes(x = reorder(State, Freq), y = Freq)) + 
                      geom_bar(stat="identity", width=0.5, fill =  "olivedrab4", 
                               color = "gold")  + coord_flip() +
                      ylab("Number of Farmers Markets by State") + xlab("") +
                      theme(axis.text.y = element_text(color = "grey20",   
                                  size = 6.5, angle = 0, face = "plain"),
                            axis.title.x = element_text(size = 14, face = "bold"), 
                            axis.title.y = element_text(size = 14, face = "bold")) +
                      geom_text(aes(label=Freq), hjust=-.3, color="blue",
                             position = position_dodge(0.9), size=2.6) +
                      scale_y_continuous(limits = c(0, 800))
  
BarchartStates      

It is natural to inspect the relationship on the number of farmers markets between county population and median income.

ggplot(All_Final_Data_Cleaned, aes(x = Total.Population, y = Median.Earnings.2010 )) + geom_point( aes(size = Freq, color = Freq)) + guides(size=FALSE) + labs(title = "Population and Income on County Farmers Markets")  + geom_label_repel(aes(label=ifelse(Freq>45 ,as.character(RowName),'')),hjust=2,vjust=3, size = 3 ) + scale_x_continuous(labels = comma) + scale_color_distiller( palette = "YlOrBr", direction = 1, name = "Farmers \nMarkets \nFreq.") 

There is a moderate positive relatioship between the number of farmers markets (size and color of bubbles) and county population. There is also a weak positive relationship between the numer of farmers markets and county 2010 median income.

ggplot(All_Final_Data_Cleaned, aes(x = AveFamilySize, y = Freq )) + geom_point() + labs(title = "Average Family Size and County Farmers Markets")  + geom_label_repel(aes(label=ifelse(Freq>45 ,as.character(RowName),'')),hjust=2,vjust=3, size = 3 ) + scale_x_continuous(labels = comma) 

Map Data

Let’s upload shape files from the Census Bureau.

G1 = left_join(Geography %>% dplyr::select(GEOID, geometry), All_Final_Data_Cleaned, by = c( "GEOID" = "Fips" )) %>% filter(State != "Hawaii")  %>% dplyr::select( -GEOID) ## add geometry shape to final data
rownames(G1) = G1$RowName 
brks = c(0,1,2,4,8,16,32,128)
tm_view()
$tm_layout
$tm_layout$style
[1] NA


attr(,"class")
[1] "tm"
tm_shape(G1) + tm_fill("Freq", breaks = brks, palette = "YlGn") + 
  tm_layout(legend.position = c(.87,.25),
            legend.text.size = .5, 
            title.position = c("center","top"),
            title = "Number of Farmers Markets by County",
            title.fontface = "bold" ,
            title.size = 1)

brks2 = c(0,2500,5000,10000,20000,50000,100000, 200000, 500000, 1000000, 3000000, 12000000)
tm_view()
$tm_layout
$tm_layout$style
[1] NA


attr(,"class")
[1] "tm"
tm_shape(G1) + tm_fill("Total.Population", breaks = brks2, palette = "BuGn") + 
  tm_layout(legend.position = c(.87,.25),
            legend.text.size = .5, 
            title.position = c("right","bottom"),
            title = "Population by County",
            title.fontface = "bold" ,
            title.size = 1) 

Add "markets per 1,000 residents’ variable.

G1$MPT = 1000* G1$Freq / G1$Total.Population
tm_view()
$tm_layout
$tm_layout$style
[1] NA


attr(,"class")
[1] "tm"
tm_shape(G1) + tm_fill("MPT", palette = "YlOrBr") + 
  tm_layout(legend.position = c(.87,.25),
            legend.text.size = .5, 
            title.position = c("center","top"),
            title = "Farmers Markets Per 1,000 Residents",
            title.fontface = "bold" ,
            title.size = 1)

PCA and Hierarchial Clustering

PCA is used to investigate the relationship between the individuals and variables.

Create a new data frame for PCA use. PCA will use Ag.District and State as supplemental qualitative variables, and Freq as a supplemental quantitiative variable. All other quantitative variables will be used to construct the dimensions.

D1 = All_Final_Data_Cleaned
rownames(D1) = All_Final_Data_Cleaned$RowName
D2 =  apply(D1[ ,c( 4:5)], 2, as.factor) %>% data.frame()
D3  = apply(D1[ ,c(6:ncol(D1))], 2, as.numeric) %>% data.frame()
D4 = cbind(D2, D3)
D4$State = as.factor(D4$State) # complete unstandarized data set
D4$Ag.District = as.factor(D4$Ag.District) # complete unstandarized data set
str(D4[ ,1:10])
'data.frame':   3114 obs. of  10 variables:
 $ State        : Factor w/ 50 levels "Alabama","Arizona",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Ag.District  : Factor w/ 85 levels "ALL COUNTIES",..: 2 11 85 76 32 2 11 32 76 32 ...
 $ Freq         : num  1 4 4 1 1 0 2 2 1 1 ...
 $ CountyArea   : num  6.26e+09 3.01e+10 7.89e+09 1.03e+10 7.32e+09 ...
 $ CountyHU     : num  72845 606886 39456 54553 104094 ...
 $ AnimalSales  : num  8.92e+06 1.88e+07 9.35e+07 1.95e+06 2.30e+08 ...
 $ AnimalCV     : num  12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7 ...
 $ CropSales    : num  1.25e+07 1.02e+08 1.21e+07 2.25e+06 1.32e+07 ...
 $ CropCV       : num  16.3 16.3 16.3 16.3 16.3 16.3 16.3 16.3 16.3 16.3 ...
 $ FruitNutSales: num  185500 2986000 435000 113000 167000 ...
apply(D4, 2, function(x) sum(is.na(x)))[apply(D4, 2, function(x) sum(is.na(x))) > 0] ##how many NA's > 0
named integer(0)

For the PCA, we assume a linear reationship between the variables. Variables are normalized by scale.unit = TRUE option. Frequency is a supplemental quanititative variable that is not used in the construction of the dimensions.

fm.pca = PCA(D4, scale.unit  = TRUE, ncp = 30, quanti.sup = 3, quali.sup = 1:2, graph = TRUE)

Here is the scree plot.

barplot(fm.pca$eig[,2][1:30], main = "scree plot",  
        ylab = "percent of total variance", 
        xlab = "first 30 principle inertia", col = "skyblue")

There are noticible drops in the variation between dimensions 5 and 6 and also dimensions 13 and 14.

plot(1:30, fm.pca$eig[1:30 ,3], pch =20, xlab = "Dim. #", ylab = "Cumulative % of Variance")
lines(1:30, fm.pca$eig[1:30 ,3])
abline( h = 80, lty =2, col = "skyblue")     

p1 = fviz_pca_biplot(fm.pca, axes = 1:2, select.ind =  list(contrib = 50),
                select.var = list(contrib = 30), 
             gradient.cols = "lightgreen", col.ind ="forestgreen",
             repel = TRUE, labelsize = 2, col.var = "orange3" , col.ind.sup = "skyblue", 
             fill.ind = "olivedrab4", 
             col.quanti.sup = "red" , fill.var = "orange3", pointsize = .7 ) + theme_minimal() +
             ggplot2::annotate("text", x = 12, y = 1, label = "Hotter", col = "red") +
             ggplot2::annotate("text", x = -12, y = 1, label = "Colder", col ="blue") +
             ggplot2::annotate("text", x = 0, y = 25, label = "Politcally Left", col = "blue") +
             ggplot2::annotate("text", x = 0, y = -15, label = "Politcally Right", col ="red")
fviz_add(p1, data.frame(fm.pca2$quali.sup$coord[1:50, ], fm.pca2$quali.sup$coord[1:50, ]), repel = TRUE, labelsize = 2.3, pointsize = .7 , color = "deepskyblue3") 

# Contributions of variables to PC1
fviz_contrib(fm.pca, choice = "var", axes = 1, top = 30)

# Contributions of variables to PC2
fviz_contrib(fm.pca, choice = "var", axes = 2, top = 30)

The first dimension consists primarily of temperature data - warmer climates are on the positive side of the x-axis while colder climates are on the negative x-axis.

The second dimesnion seperates politcally left leaning counties on the positive side of the y-axis, with politically right leaning counties on the negative y-axis. Most of the “blue states” are above the x-axis, while “red states” are below the x-axis.

Variables, states, and/or counties near each other on the biplot between the first and second dimensions can be interprested as similar with respect to temperature and politcal leaning. For example, Los Angeles County, located near the top of the plot, has a large y-value, implying it is very left leaning politcally. It’s moderate positiion on the x-axis indicates the temperatures are moderately above average.

p2 = fviz_pca_biplot(fm.pca, axes = 3:4, select.ind =  list(contrib = 50),
                select.var = list(contrib = 30), 
             gradient.cols = "lightgreen", col.ind ="forestgreen",
             repel = TRUE, labelsize = 2, col.var = "orange3" , col.ind.sup = "skyblue", 
             fill.ind = "olivedrab4", 
             col.quanti.sup = "red" , fill.var = "orange3", pointsize = .7 ) + theme_minimal() +
             ggplot2::annotate("text", x = 9, y = 1, label = "Large Families", col = "red") +
             ggplot2::annotate("text", x = -9, y = 1, label = "Small Families", col ="blue") +
             ggplot2::annotate("text", x = 0, y = 10, label = "Wetter", col = "blue") +
             ggplot2::annotate("text", x = 0, y = -20, label = "Drier", col ="red")
fviz_add(p2, data.frame(fm.pca2$quali.sup$coord[1:50, ], fm.pca2$quali.sup$coord[1:50, ]), repel = TRUE, labelsize = 2.3, pointsize = .7 , color = "deepskyblue3") 

# Contributions of variables to PC3
fviz_contrib(fm.pca, choice = "var", axes = 3, top = 30)

# Contributions of variables to PC4
fviz_contrib(fm.pca, choice = "var", axes = 4, top = 30)

The thrid dimension largely seperates family size. The fourth dimesnsion seperates precipation.

p3 = fviz_pca_biplot(fm.pca, axes = 5:6, select.ind =  list(contrib = 50),
                select.var = list(contrib = 30), 
             gradient.cols = "lightgreen", col.ind ="forestgreen",
             repel = TRUE, labelsize = 2, col.var = "orange3" , col.ind.sup = "skyblue", 
             fill.ind = "olivedrab4", 
             col.quanti.sup = "red" , fill.var = "orange3", pointsize = .7 ) + theme_minimal() +
             ggplot2::annotate("text", x = 15, y = 1, label = "Large Families", col = "red") +
             ggplot2::annotate("text", x = -15, y = 1, label = "Small Families", col ="blue") +
             ggplot2::annotate("text", x = 0, y = 20, label = "More Diverse", col = "blue") +
             ggplot2::annotate("text", x = 0, y = -15, label = "Less Diverse", col ="red")
fviz_add(p3, data.frame(fm.pca2$quali.sup$coord[1:50, ], fm.pca2$quali.sup$coord[1:50, ]), repel = TRUE, labelsize = 2.3, pointsize = .7 , color = "deepskyblue3") 

# Contributions of variables to PC5
fviz_contrib(fm.pca, choice = "var", axes = 5, top = 30)

# Contributions of variables to PC6
fviz_contrib(fm.pca, choice = "var", axes = 6, top = 30)

All_Data_PCA = data.frame(cbind(fm.pca$ind$coord ,All_Final_Data_Cleaned$Freq)) %>% dplyr::select(V31, Dim.1:Dim.30) %>% rename(Freq = V31) 
write.csv(All_Data_PCA, "All_Data_PCA.csv")

CLUSTERING

Goal- identify variables that best seperate county profiles

Choose 7 clusters (Drop in inertia gain). Choose best variables to represent each cluster.

fm.hcpc<-HCPC(fm.pca ,nb.clust=7 ,graph=TRUE, description = TRUE) 

print("########## CLUSTER 1 ##########")
[1] "########## CLUSTER 1 ##########"
round(fm.hcpc$desc.var$quanti[[1]][ ,c(1,2,3,6)],3)
                                                            v.test Mean in category  Overall mean p.value
elevation                                                   26.763     9.629030e+02  3.990520e+02   0.000
Male55to59                                                  26.585     8.500000e-02  7.000000e-02   0.000
MaleOver59                                                  25.239     2.660000e-01  2.040000e-01   0.000
Median.Age                                                  23.284     4.520300e+01  3.990300e+01   0.000
MarriedHouseholdRatio                                       22.177     8.370000e-01  7.630000e-01   0.000
Female55to59                                                21.935     8.100000e-02  7.000000e-02   0.000
lat                                                         21.824     4.322800e+01  3.825200e+01   0.000
FemaleOver59                                                20.643     2.900000e-01  2.390000e-01   0.000
TempRange                                                   19.594     2.576630e+02  2.297520e+02   0.000
AgLandAcres                                                 19.230     2.120111e+05  7.235322e+04   0.000
Farming.fishing.and.forestry.occupations                    18.347     4.330000e+00  2.110000e+00   0.000
At.Least.High.School.Diploma                                15.141     8.832300e+01  8.300900e+01   0.000
Libertarians.2016                                           15.031     4.229000e+00  3.163000e+00   0.000
Sire.Homogeneity                                            14.733     8.460000e-01  7.190000e-01   0.000
Female44to54                                                14.430     1.590000e-01  1.490000e-01   0.000
CropCV                                                      14.381     2.217100e+01  1.774700e+01   0.000
S                                                           13.912     2.610000e-01 -3.460000e-01   0.000
Management.professional.and.related.occupations             13.899     3.398400e+01  2.982700e+01   0.000
White                                                       13.364     9.110900e+01  7.903500e+01   0.000
White..Asian                                                12.900     9.149100e+01  8.010600e+01   0.000
Male44to54                                                  12.570     1.600000e-01  1.500000e-01   0.000
Republicans.2012                                            12.428     6.822600e+01  5.964100e+01   0.000
Republicans.2016                                            12.403     7.264300e+01  6.359700e+01   0.000
CA                                                          11.285     5.300000e-01  1.000000e-02   0.000
Republicans.2008                                            11.075     6.395000e+01  5.678500e+01   0.000
Injury.deaths                                                8.147     8.439300e+01  7.575400e+01   0.000
MAR                                                          5.961     8.040000e-01  7.350000e-01   0.000
CFS                                                          5.496     0.000000e+00  0.000000e+00   0.000
CountyArea                                                   4.620     1.814377e+10  1.200905e+10   0.000
Max.Alc                                                      3.711     1.000000e-03  1.000000e-03   0.000
Green.2016                                                   3.543     9.490000e-01  8.500000e-01   0.000
AgLandCV                                                     2.186     2.523400e+01  2.381600e+01   0.029
Construction.extraction.maintenance.and.repair.occupations   2.041     1.184000e+01  1.151900e+01   0.041
At.Least.Bachelors.s.Degree                                  2.011     1.980700e+01  1.899500e+01   0.044
AnimalCV                                                     1.982     1.321300e+01  1.256700e+01   0.048
CountyHU                                                    -2.255     2.303762e+04  6.297451e+05   0.024
ACFS                                                        -2.281     0.000000e+00  0.000000e+00   0.023
OtherRace                                                   -2.478     1.374000e+00  1.582000e+00   0.013
Service.occupations                                         -2.940     1.696900e+01  1.744900e+01   0.003
VeggieCV                                                    -3.150     1.619800e+01  1.838500e+01   0.002
HusbandWifeFamilyRatio                                      -3.187     2.720000e-01  2.790000e-01   0.001
Adults.65.and.Older.Living.in.Poverty                       -4.411     1.040200e+01  1.152600e+01   0.000
Median.Earnings.2010                                        -5.036     2.425283e+04  2.543765e+04   0.000
Precincts                                                   -5.268     1.240200e+01  5.489400e+01   0.000
Hispanic                                                    -5.312     4.716000e+00  7.940000e+00   0.000
Graduate.Degree                                             -5.420     5.470000e+00  6.445000e+00   0.000
Total.Population                                            -5.966     1.075573e+04  9.775404e+04   0.000
Preschool.Enrollment.Ratio.enrolled.ages.3.and.4            -6.028     3.892700e+01  4.298400e+01   0.000
Freq                                                        -6.279     1.050000e+00  2.790000e+00   0.000
Asian                                                       -6.543     3.830000e-01  1.071000e+00   0.000
RenterOccupied                                              -6.860     2.520000e-01  2.770000e-01   0.000
Votes                                                       -6.890     5.287850e+03  4.174833e+04   0.000
Gini.Coefficient                                            -7.206     4.190000e-01  4.320000e-01   0.000
TotalFemale                                                 -7.403     4.930000e-01  5.010000e-01   0.000
FruitNutCV                                                  -8.090     1.754400e+01  2.496300e+01   0.000
HIV.prevalence.rate                                         -8.129     7.211000e+01  1.491930e+02   0.000
Children.Under.6.Living.in.Poverty                          -9.330     1.969800e+01  2.487600e+01   0.000
Child.Poverty.living.in.families.below.the.poverty.line    -10.187     1.660000e+01  2.114500e+01   0.000
Poverty.Rate.below.federal.poverty.threshold               -10.741     1.228200e+01  1.547800e+01   0.000
Adult.obesity                                              -11.299     2.830000e-01  3.060000e-01   0.000
Sexually.transmitted.infections                            -11.305     2.089210e+02  3.433310e+02   0.000
Low.birthweight                                            -11.587     7.200000e-02  8.300000e-02   0.000
Violent.crime                                              -12.094     1.365740e+02  2.514570e+02   0.000
FamilyRatio                                                -12.226     6.490000e-01  6.780000e-01   0.000
Adult.smoking                                              -12.281     1.770000e-01  2.110000e-01   0.000
Democrats.2008                                             -12.370     3.355300e+01  4.156800e+01   0.000
Black                                                      -12.545     3.750000e-01  8.830000e+00   0.000
Diabetes                                                   -12.984     9.400000e-02  1.070000e-01   0.000
FemaleUnder20                                              -13.154     2.330000e-01  2.540000e-01   0.000
Summer.Tmax                                                -13.181     8.266970e+02  8.571330e+02   0.000
Democrats.2012                                             -13.607     2.911000e+01  3.851200e+01   0.000
Male20to24                                                 -13.833     4.500000e-02  6.300000e-02   0.000
Production.transportation.and.material.moving.occupations  -13.848     1.238000e+01  1.625200e+01   0.000
Teen.births                                                -13.897     3.120900e+01  4.408800e+01   0.000
MaleUnder20                                                -14.393     2.440000e-01  2.700000e-01   0.000
Sales.and.office.occupations                               -14.523     2.049500e+01  2.284300e+01   0.000
Poor.physical.health.days                                  -14.614     3.059000e+00  3.807000e+00   0.000
DemChange12                                                -14.782    -2.879300e+01 -1.946300e+01   0.000
Democrats.2016                                             -14.848     2.106500e+01  3.169000e+01   0.000
Poor.mental.health.days                                    -15.165     2.858000e+00  3.536000e+00   0.000
Female20to24                                               -15.192     3.900000e-02  5.700000e-02   0.000
Less.Than.High.School.Diploma                              -15.654     1.155200e+01  1.691100e+01   0.000
AveFamilySize                                              -16.364     2.803000e+00  2.923000e+00   0.000
Children.in.single.parent.households                       -16.585     2.360000e-01  3.160000e-01   0.000
Unemployment                                               -16.860     5.500000e-02  7.700000e-02   0.000
DemChange08                                                -17.476    -3.919300e+01 -2.615100e+01   0.000
Winter.Tmax                                                -18.035     3.526570e+02  4.490200e+02   0.000
Annual.Tmax                                                -18.662     5.934230e+02  6.619730e+02   0.000
Spring.Tmax                                                -19.405     5.853120e+02  6.591830e+02   0.000
AveHousehouldSize                                          -20.053     2.284000e+00  2.479000e+00   0.000
Male25to44                                                 -20.642     2.070000e-01  2.430000e-01   0.000
Autumn.Tmax                                                -20.769     6.062780e+02  6.799320e+02   0.000
Summer.Prcp                                                -20.796     7.284220e+02  1.108740e+03   0.000
Winter.Tavg                                                -20.883     2.415120e+02  3.451100e+02   0.000
Summer.Tavg                                                -21.830     6.833990e+02  7.396110e+02   0.000
lon                                                        -22.407    -1.040800e+02 -9.176500e+01   0.000
Spring.Tavg                                                -22.621     4.559260e+02  5.392730e+02   0.000
Annual.Tavg                                                -23.129     4.643930e+02  5.472120e+02   0.000
temp                                                       -23.129     8.022000e+00  1.262300e+01   0.000
Winter.Prcp                                                -23.547     2.500120e+02  8.071530e+02   0.000
Winter.Tmin                                                -23.555     1.290690e+02  2.404230e+02   0.000
Female25to44                                               -23.615     1.970000e-01  2.310000e-01   0.000
Autumn.Tavg                                                -25.089     4.740800e+02  5.615880e+02   0.000
Spring.Tmin                                                -25.212     3.258320e+02  4.187250e+02   0.000
Summer.Tmin                                                -26.287     5.413290e+02  6.224800e+02   0.000
Annual.Tmin                                                -26.458     3.357600e+02  4.322210e+02   0.000
Spring.Prcp                                                -27.327     5.734290e+02  1.020002e+03   0.000
Autumn.Tmin                                                -28.053     3.419450e+02  4.431530e+02   0.000
precip                                                     -29.366     5.069650e+02  9.825080e+02   0.000
Autumn.Prcp                                                -30.453     4.426820e+02  9.303860e+02   0.000
print(c("########## CLUSTER 2 ##########"))
[1] "########## CLUSTER 2 ##########"
round(fm.hcpc$desc.var$quanti[[2]][ ,c(1,2,3,6)],3)
                                                            v.test Mean in category Overall mean p.value
At.Least.Bachelors.s.Degree                                 32.624           33.993       18.995   0.000
Graduate.Degree                                             29.911           12.569        6.445   0.000
Median.Earnings.2010                                        28.148        32975.998    25437.655   0.000
Management.professional.and.related.occupations             25.390           38.472       29.827   0.000
S                                                           24.771            0.885       -0.346   0.000
HusbandWifeFamilyRatio                                      21.833            0.338        0.279   0.000
At.Least.High.School.Diploma                                19.594           90.838       83.009   0.000
DemChange12                                                 18.277           -6.331      -19.463   0.000
CA                                                          17.245            0.915        0.010   0.000
DemChange08                                                 16.772          -11.902      -26.151   0.000
Democrats.2016                                              16.049           44.763       31.690   0.000
School.Enrollment                                           15.881           79.375       74.986   0.000
Female25to44                                                15.574            0.255        0.231   0.000
Asian                                                       15.505            2.926        1.071   0.000
Freq                                                        14.812            7.462        2.790   0.000
Sales.and.office.occupations                                13.967           25.415       22.843   0.000
Green.2016                                                  12.924            1.260        0.850   0.000
Votes                                                       12.403       116467.440    41748.328   0.000
Libertarians.2016                                           12.202            4.148        3.163   0.000
Democrats.2008                                              12.065           50.467       41.568   0.000
ACFS                                                        11.866            0.000        0.000   0.000
Democrats.2012                                              11.813           47.804       38.512   0.000
lat                                                         10.946           41.093       38.252   0.000
Preschool.Enrollment.Ratio.enrolled.ages.3.and.4            10.166           50.770       42.984   0.000
AveFamilySize                                               10.138            3.007        2.923   0.000
Male25to44                                                   9.268            0.261        0.243   0.000
Total.Population                                             8.490       238689.182    97754.035   0.000
Mean.Alc                                                     8.015            0.000        0.000   0.000
Female44to54                                                 7.813            0.155        0.149   0.000
MarriedHouseholdRatio                                        7.330            0.791        0.763   0.000
Precincts                                                    7.237          121.346       54.894   0.000
AveHousehouldSize                                            6.956            2.556        2.479   0.000
Female20to24                                                 6.915            0.066        0.057   0.000
lon                                                          6.200          -87.886      -91.765   0.000
Male20to24                                                   5.902            0.072        0.063   0.000
White..Asian                                                 5.614           85.747       80.106   0.000
Mixedness                                                    5.568            0.240       -0.010   0.000
MaleUnder20                                                  5.048            0.280        0.270   0.000
Male44to54                                                   4.730            0.154        0.150   0.000
TotalFemale                                                  4.530            0.506        0.501   0.000
FemaleUnder20                                                3.940            0.261        0.254   0.000
White                                                        3.680           82.820       79.035   0.000
Autumn.Prcp                                                  3.405          992.461      930.386   0.001
OtherRace                                                    3.295            1.896        1.582   0.001
Max.Alc                                                      2.626            0.001        0.001   0.009
Hispanic                                                    -2.087            6.498        7.940   0.037
Amerindian                                                  -3.371            0.410        1.541   0.001
Violent.crime                                               -3.540          213.182      251.457   0.000
Black                                                       -4.410            5.447        8.830   0.000
AgLandAcres                                                 -4.633        34051.368    72353.224   0.000
Sexually.transmitted.infections                             -4.663          280.219      343.331   0.000
FruitNutCV                                                  -4.739           20.016       24.963   0.000
Gini.Coefficient                                            -4.988            0.422        0.432   0.000
Female55to59                                                -5.016            0.067        0.070   0.000
CropCV                                                      -5.549           15.803       17.747   0.000
Male55to59                                                  -5.610            0.066        0.070   0.000
MAR                                                         -5.735            0.658        0.735   0.000
Autumn.Tmin                                                 -7.495          412.369      443.153   0.000
Winter.Tmin                                                 -7.867          198.085      240.423   0.000
Unemployment                                                -8.009            0.065        0.077   0.000
TempRange                                                   -8.079          216.651      229.752   0.000
Summer.Tmin                                                 -8.328          593.212      622.480   0.000
RepChange08                                                 -8.525            1.930       11.582   0.000
Annual.Tmin                                                 -8.557          396.708      432.221   0.000
Winter.Tavg                                                 -9.206          293.124      345.110   0.000
Median.Age                                                  -9.295           37.494       39.903   0.000
Spring.Tmin                                                 -9.449          379.092      418.725   0.000
Autumn.Tavg                                                 -9.459          524.032      561.588   0.000
Service.occupations                                         -9.690           15.649       17.449   0.000
Low.birthweight                                             -9.969            0.072        0.083   0.000
temp                                                       -10.173           10.319       12.623   0.000
Annual.Tavg                                                -10.173          505.744      547.212   0.000
Winter.Tmax                                                -10.227          386.817      449.020   0.000
Poor.mental.health.days                                    -10.258            3.014        3.536   0.000
Farming.fishing.and.forestry.occupations                   -10.665            0.642        2.110   0.000
Summer.Tavg                                                -10.698          708.250      739.611   0.000
Spring.Tavg                                                -11.001          493.130      539.273   0.000
Autumn.Tmax                                                -11.228          634.603      679.932   0.000
Annual.Tmax                                                -11.626          613.359      661.973   0.000
Children.in.single.parent.households                       -11.917            0.250        0.316   0.000
Republicans.2008                                           -12.016           47.937       56.785   0.000
Republicans.2012                                           -12.024           50.185       59.641   0.000
RepChange12                                                -12.056           -2.645        7.502   0.000
Spring.Tmax                                                -12.239          606.145      659.183   0.000
Summer.Tmax                                                -12.459          824.384      857.133   0.000
Poor.physical.health.days                                  -14.152            2.983        3.807   0.000
Construction.extraction.maintenance.and.repair.occupations -14.299            8.959       11.519   0.000
MaleOver59                                                 -14.849            0.162        0.204   0.000
FemaleOver59                                               -15.952            0.195        0.239   0.000
Adults.65.and.Older.Living.in.Poverty                      -16.752            6.667       11.526   0.000
Adult.smoking                                              -16.799            0.158        0.211   0.000
Production.transportation.and.material.moving.occupations  -16.922           10.865       16.252   0.000
Republicans.2016                                           -17.618           48.968       63.597   0.000
Adult.obesity                                              -17.865            0.265        0.306   0.000
Poverty.Rate.below.federal.poverty.threshold               -19.152            8.990       15.478   0.000
Children.Under.6.Living.in.Poverty                         -19.254           12.711       24.876   0.000
CFS                                                        -19.428            0.000        0.000   0.000
Diabetes                                                   -19.644            0.084        0.107   0.000
Less.Than.High.School.Diploma                              -19.883            9.162       16.911   0.000
Child.Poverty.living.in.families.below.the.poverty.line    -20.694           10.635       21.145   0.000
Teen.births                                                -20.834           22.109       44.088   0.000
Injury.deaths                                              -20.949           50.468       75.754   0.000
Uninsured                                                  -20.981            0.119        0.179   0.000
print(c("########## CLUSTER 3 ##########"))
[1] "########## CLUSTER 3 ##########"
round(fm.hcpc$desc.var$quanti[[3]][ ,c(1,2,3,6)],3)
                                                            v.test Mean in category  Overall mean p.value
Sire.Homogeneity                                            26.019     8.600000e-01  7.190000e-01   0.000
White                                                       23.354     9.224400e+01  7.903500e+01   0.000
White..Asian                                                22.972     9.279900e+01  8.010600e+01   0.000
lat                                                         20.721     4.121000e+01  3.825200e+01   0.000
RepChange12                                                 18.509     1.607000e+01  7.502000e+00   0.000
Production.transportation.and.material.moving.occupations   16.718     1.917900e+01  1.625200e+01   0.000
RepChange08                                                 16.366     2.177200e+01  1.158200e+01   0.000
At.Least.High.School.Diploma                                13.246     8.592000e+01  8.300900e+01   0.000
S                                                           12.612    -1.000000e-03 -3.460000e-01   0.000
lon                                                         11.612    -8.777000e+01 -9.176500e+01   0.000
Male44to54                                                  11.589     1.560000e-01  1.500000e-01   0.000
Median.Age                                                  11.559     4.155000e+01  3.990300e+01   0.000
MarriedHouseholdRatio                                       11.361     7.870000e-01  7.630000e-01   0.000
FemaleOver59                                                10.793     2.560000e-01  2.390000e-01   0.000
CA                                                          10.440     3.110000e-01  1.000000e-02   0.000
Libertarians.2016                                            9.635     3.591000e+00  3.163000e+00   0.000
Male55to59                                                   9.459     7.300000e-02  7.000000e-02   0.000
Female44to54                                                 8.889     1.530000e-01  1.490000e-01   0.000
Democrats.2008                                               8.377     4.496600e+01  4.156800e+01   0.000
MaleOver59                                                   8.365     2.170000e-01  2.040000e-01   0.000
Summer.Prcp                                                  7.950     1.199760e+03  1.108740e+03   0.000
Green.2016                                                   6.900     9.700000e-01  8.500000e-01   0.000
Female55to59                                                 6.223     7.200000e-02  7.000000e-02   0.000
CFS                                                          5.792     0.000000e+00  0.000000e+00   0.000
Democrats.2012                                               5.156     4.074300e+01  3.851200e+01   0.000
Autumn.Prcp                                                  4.964     9.801540e+02  9.303860e+02   0.000
Spring.Prcp                                                  4.923     1.070365e+03  1.020002e+03   0.000
AgLandCV                                                     4.612     2.569000e+01  2.381600e+01   0.000
Adult.smoking                                                4.470     2.190000e-01  2.110000e-01   0.000
Republicans.2016                                             3.774     6.532000e+01  6.359700e+01   0.000
Adult.obesity                                                3.283     3.100000e-01  3.060000e-01   0.001
School.Enrollment                                            2.584     7.537800e+01  7.498600e+01   0.010
precip                                                       2.077     1.003565e+03  9.825080e+02   0.038
VeggieSales                                                 -2.139     2.536536e+06  6.327674e+06   0.032
AnimalCV                                                    -2.178     1.212300e+01  1.256700e+01   0.029
FruitNutSales                                               -2.544     1.887515e+06  9.235039e+06   0.011
Freq                                                        -2.705     2.321000e+00  2.790000e+00   0.007
Diabetes                                                    -2.912     1.050000e-01  1.070000e-01   0.004
CountyHU                                                    -3.312     7.186757e+04  6.297451e+05   0.001
HusbandWifeFamilyRatio                                      -3.849     2.730000e-01  2.790000e-01   0.000
FamilyRatio                                                 -3.859     6.720000e-01  6.780000e-01   0.000
Construction.extraction.maintenance.and.repair.occupations  -4.072     1.111800e+01  1.151900e+01   0.000
Precincts                                                   -4.138     3.399900e+01  5.489400e+01   0.000
Amerindian                                                  -4.375     7.340000e-01  1.541000e+00   0.000
Poor.mental.health.days                                     -4.405     3.413000e+00  3.536000e+00   0.000
Democrats.2016                                              -4.699     2.958500e+01  3.169000e+01   0.000
OtherRace                                                   -5.061     1.317000e+00  1.582000e+00   0.000
AveFamilySize                                               -5.550     2.898000e+00  2.923000e+00   0.000
Republicans.2012                                            -5.702     5.717500e+01  5.964100e+01   0.000
Total.Population                                            -5.864     4.422103e+04  9.775404e+04   0.000
Poor.physical.health.days                                   -5.880     3.619000e+00  3.807000e+00   0.000
Female20to24                                                -6.137     5.300000e-02  5.700000e-02   0.000
Male20to24                                                  -6.400     5.800000e-02  6.300000e-02   0.000
Votes                                                       -6.606     1.986192e+04  4.174833e+04   0.000
CountyArea                                                  -6.842     6.320820e+09  1.200905e+10   0.000
Graduate.Degree                                             -6.984     5.659000e+00  6.445000e+00   0.000
Winter.Prcp                                                 -7.208     7.003780e+02  8.071530e+02   0.000
Male25to44                                                  -7.286     2.350000e-01  2.430000e-01   0.000
Children.Under.6.Living.in.Poverty                          -7.731     2.219000e+01  2.487600e+01   0.000
Asian                                                       -7.839     5.550000e-01  1.071000e+00   0.000
elevation                                                   -7.891     2.949680e+02  3.990520e+02   0.000
Children.in.single.parent.households                        -8.015     2.920000e-01  3.160000e-01   0.000
Injury.deaths                                               -8.078     7.039200e+01  7.575400e+01   0.000
Farming.fishing.and.forestry.occupations                    -8.138     1.494000e+00  2.110000e+00   0.000
MaleUnder20                                                 -8.175     2.610000e-01  2.700000e-01   0.000
At.Least.Bachelors.s.Degree                                 -8.607     1.681900e+01  1.899500e+01   0.000
Republicans.2008                                            -8.987     5.314600e+01  5.678500e+01   0.000
FemaleUnder20                                               -9.544     2.450000e-01  2.540000e-01   0.000
Female25to44                                                -9.707     2.220000e-01  2.310000e-01   0.000
Management.professional.and.related.occupations             -9.856     2.798100e+01  2.982700e+01   0.000
FruitNutCV                                                 -10.173     1.912200e+01  2.496300e+01   0.000
Child.Poverty.living.in.families.below.the.poverty.line    -10.364     1.825000e+01  2.114500e+01   0.000
AveHousehouldSize                                          -10.550     2.415000e+00  2.479000e+00   0.000
HIV.prevalence.rate                                        -10.769     8.525900e+01  1.491930e+02   0.000
AgLandAcres                                                -10.995     2.236444e+04  7.235322e+04   0.000
Summer.Tmin                                                -11.837     5.996020e+02  6.224800e+02   0.000
Violent.crime                                              -12.178     1.790380e+02  2.514570e+02   0.000
ACFS                                                       -12.647     0.000000e+00  0.000000e+00   0.000
Poverty.Rate.below.federal.poverty.threshold               -12.767     1.309900e+01  1.547800e+01   0.000
Adults.65.and.Older.Living.in.Poverty                      -13.014     9.450000e+00  1.152600e+01   0.000
RenterOccupied                                             -13.144     2.470000e-01  2.770000e-01   0.000
Less.Than.High.School.Diploma                              -13.206     1.408000e+01  1.691100e+01   0.000
Hispanic                                                   -13.435     2.836000e+00  7.940000e+00   0.000
Max.Alc                                                    -13.453     0.000000e+00  1.000000e-03   0.000
CropCV                                                     -13.506     1.514500e+01  1.774700e+01   0.000
Teen.births                                                -14.516     3.566600e+01  4.408800e+01   0.000
Autumn.Tmin                                                -14.914     4.094680e+02  4.431530e+02   0.000
Black                                                      -15.440     2.316000e+00  8.830000e+00   0.000
Sexually.transmitted.infections                            -15.554     2.275570e+02  3.433310e+02   0.000
Low.birthweight                                            -15.982     7.300000e-02  8.300000e-02   0.000
Mean.Alc                                                   -16.261     0.000000e+00  0.000000e+00   0.000
Gini.Coefficient                                           -16.451     4.140000e-01  4.320000e-01   0.000
Annual.Tmin                                                -17.279     3.927830e+02  4.322210e+02   0.000
Summer.Tavg                                                -17.917     7.107270e+02  7.396110e+02   0.000
Spring.Tmin                                                -18.158     3.768410e+02  4.187250e+02   0.000
Mixedness                                                  -18.431    -4.650000e-01 -1.000000e-02   0.000
MAR                                                        -18.645     5.980000e-01  7.350000e-01   0.000
DemChange08                                                -18.752    -3.491200e+01 -2.615100e+01   0.000
Autumn.Tavg                                                -19.166     5.197380e+02  5.615880e+02   0.000
TempRange                                                  -19.182     2.126460e+02  2.297520e+02   0.000
Winter.Tmin                                                -19.921     1.814670e+02  2.404230e+02   0.000
Spring.Tavg                                                -21.130     4.905320e+02  5.392730e+02   0.000
temp                                                       -21.194     9.983000e+00  1.262300e+01   0.000
Annual.Tavg                                                -21.194     4.997030e+02  5.472120e+02   0.000
DemChange12                                                -21.296    -2.787800e+01 -1.946300e+01   0.000
Uninsured                                                  -22.637     1.430000e-01  1.790000e-01   0.000
Autumn.Tmax                                                -22.781     6.293550e+02  6.799320e+02   0.000
Winter.Tavg                                                -23.188     2.730960e+02  3.451100e+02   0.000
Spring.Tmax                                                -23.593     6.029570e+02  6.591830e+02   0.000
Summer.Tmax                                                -23.833     8.226800e+02  8.571330e+02   0.000
Annual.Tmax                                                -24.588     6.054300e+02  6.619730e+02   0.000
Winter.Tmax                                                -25.445     3.639070e+02  4.490200e+02   0.000
print(c("########## CLUSTER 4 ##########"))
[1] "########## CLUSTER 4 ##########"
round(fm.hcpc$desc.var$quanti[[4]][ ,c(1,2,3,6)],3)
                                                            v.test Mean in category  Overall mean p.value
Hispanic                                                    28.895     3.336800e+01  7.940000e+00   0.000
FemaleUnder20                                               27.376     3.160000e-01  2.540000e-01   0.000
AveHousehouldSize                                           24.650     2.827000e+00  2.479000e+00   0.000
TempRange                                                   22.294     2.757970e+02  2.297520e+02   0.000
AveFamilySize                                               22.232     3.159000e+00  2.923000e+00   0.000
elevation                                                   22.208     1.077480e+03  3.990520e+02   0.000
AgLandAcres                                                 21.717     3.010351e+05  7.235322e+04   0.000
MaleUnder20                                                 20.934     3.230000e-01  2.700000e-01   0.000
HusbandWifeFamilyRatio                                      15.247     3.320000e-01  2.790000e-01   0.000
Uninsured                                                   15.075     2.340000e-01  1.790000e-01   0.000
AnimalSales                                                 14.905     1.962571e+08  6.295049e+07   0.000
Farming.fishing.and.forestry.occupations                    14.637     4.678000e+00  2.110000e+00   0.000
Teen.births                                                 13.589     6.234900e+01  4.408800e+01   0.000
FamilyRatio                                                 13.249     7.230000e-01  6.780000e-01   0.000
Amerindian                                                  13.061     7.126000e+00  1.541000e+00   0.000
CropSales                                                   12.439     2.000772e+08  6.220057e+07   0.000
FruitNutSales                                               12.230     9.105897e+07  9.235039e+06   0.000
CountyArea                                                  12.213     3.552485e+10  1.200905e+10   0.000
CropCV                                                      11.102     2.269900e+01  1.774700e+01   0.000
VeggieSales                                                 10.324     4.870766e+07  6.327674e+06   0.000
CFS                                                         10.091     0.000000e+00  0.000000e+00   0.000
Less.Than.High.School.Diploma                               10.056     2.190200e+01  1.691100e+01   0.000
MAR                                                          9.487     8.950000e-01  7.350000e-01   0.000
DemChange12                                                  8.949    -1.127300e+01 -1.946300e+01   0.000
Libertarians.2016                                            8.710     4.059000e+00  3.163000e+00   0.000
RenterOccupied                                               7.518     3.150000e-01  2.770000e-01   0.000
Summer.Tmax                                                  7.227     8.813320e+02  8.571330e+02   0.000
Poverty.Rate.below.federal.poverty.threshold                 5.968     1.805300e+01  1.547800e+01   0.000
DemChange08                                                  5.869    -1.980000e+01 -2.615100e+01   0.000
Female25to44                                                 5.792     2.420000e-01  2.310000e-01   0.000
Mixedness                                                    5.591     3.100000e-01 -1.000000e-02   0.000
Construction.extraction.maintenance.and.repair.occupations   4.782     1.260900e+01  1.151900e+01   0.000
Child.Poverty.living.in.families.below.the.poverty.line      4.629     2.413900e+01  2.114500e+01   0.000
Male25to44                                                   4.628     2.540000e-01  2.430000e-01   0.000
Male20to24                                                   3.936     7.100000e-02  6.300000e-02   0.000
Female20to24                                                 3.698     6.300000e-02  5.700000e-02   0.000
ACFS                                                         3.531     0.000000e+00  0.000000e+00   0.000
Max.Alc                                                      3.469     1.000000e-03  1.000000e-03   0.001
Republicans.2008                                             3.399     5.997400e+01  5.678500e+01   0.001
Injury.deaths                                                3.346     8.089900e+01  7.575400e+01   0.001
Service.occupations                                          3.203     1.820700e+01  1.744900e+01   0.001
Children.Under.6.Living.in.Poverty                           3.058     2.733800e+01  2.487600e+01   0.002
Annual.Tmax                                                  3.050     6.782190e+02  6.619730e+02   0.002
Sexually.transmitted.infections                              3.028     3.955290e+02  3.433310e+02   0.002
Winter.Tmax                                                  2.632     4.694070e+02  4.490200e+02   0.009
Republicans.2012                                             2.602     6.224700e+01  5.964100e+01   0.009
Green.2016                                                   2.599     9.550000e-01  8.500000e-01   0.009
Spring.Tmax                                                  2.567     6.733500e+02  6.591830e+02   0.010
FruitNutCV                                                   2.409     2.816700e+01  2.496300e+01   0.016
Adults.65.and.Older.Living.in.Poverty                        2.059     1.228600e+01  1.152600e+01   0.039
Winter.Tmin                                                 -2.044     2.264120e+02  2.404230e+02   0.041
Votes                                                       -2.074     2.583265e+04  4.174833e+04   0.038
Summer.Tavg                                                 -2.112     7.317270e+02  7.396110e+02   0.035
Freq                                                        -2.357     1.843000e+00  2.790000e+00   0.018
MarriedHouseholdRatio                                       -2.709     7.490000e-01  7.630000e-01   0.007
Republicans.2016                                            -2.889     6.054100e+01  6.359700e+01   0.004
Democrats.2012                                              -2.927     3.557900e+01  3.851200e+01   0.003
Autumn.Tavg                                                 -3.006     5.463860e+02  5.615880e+02   0.003
Gini.Coefficient                                            -3.074     4.240000e-01  4.320000e-01   0.002
Management.professional.and.related.occupations             -3.158     2.845700e+01  2.982700e+01   0.002
AgLandCV                                                    -3.524     2.050000e+01  2.381600e+01   0.000
Democrats.2008                                              -3.580     3.820400e+01  4.156800e+01   0.000
Low.birthweight                                             -3.647     7.800000e-02  8.300000e-02   0.000
Sales.and.office.occupations                                -3.796     2.195300e+01  2.284300e+01   0.000
S                                                           -3.845    -5.890000e-01 -3.460000e-01   0.000
At.Least.Bachelors.s.Degree                                 -3.978     1.666600e+01  1.899500e+01   0.000
HIV.prevalence.rate                                         -4.236     9.095400e+01  1.491930e+02   0.000
Graduate.Degree                                             -4.605     5.244000e+00  6.445000e+00   0.000
Adult.obesity                                               -4.679     2.920000e-01  3.060000e-01   0.000
Median.Earnings.2010                                        -4.836     2.378801e+04  2.543765e+04   0.000
Spring.Tmin                                                 -5.102     3.914700e+02  4.187250e+02   0.000
Production.transportation.and.material.moving.occupations   -5.330     1.409100e+01  1.625200e+01   0.000
Preschool.Enrollment.Ratio.enrolled.ages.3.and.4            -5.586     3.753300e+01  4.298400e+01   0.000
Annual.Tmin                                                 -5.637     4.024220e+02  4.322210e+02   0.000
Adult.smoking                                               -5.696     1.880000e-01  2.110000e-01   0.000
School.Enrollment                                           -6.185     7.280800e+01  7.498600e+01   0.000
RepChange08                                                 -6.772     1.816000e+00  1.158200e+01   0.000
Autumn.Tmin                                                 -7.105     4.059840e+02  4.431530e+02   0.000
TotalFemale                                                 -7.209     4.900000e-01  5.010000e-01   0.000
Black                                                       -7.303     1.693000e+00  8.830000e+00   0.000
CA                                                          -8.030    -5.270000e-01  1.000000e-02   0.000
RepChange12                                                 -8.418    -1.523000e+00  7.502000e+00   0.000
Summer.Tmin                                                 -8.895     5.826660e+02  6.224800e+02   0.000
At.Least.High.School.Diploma                                -9.651     7.809800e+01  8.300900e+01   0.000
Diabetes                                                   -10.160     9.200000e-02  1.070000e-01   0.000
MaleOver59                                                 -10.200     1.680000e-01  2.040000e-01   0.000
AnimalCV                                                   -10.453     7.628000e+00  1.256700e+01   0.000
Winter.Prcp                                                -13.716     3.365840e+02  8.071530e+02   0.000
Male55to59                                                 -13.825     5.900000e-02  7.000000e-02   0.000
Sire.Homogeneity                                           -14.058     5.440000e-01  7.190000e-01   0.000
Female55to59                                               -14.666     6.000000e-02  7.000000e-02   0.000
FemaleOver59                                               -14.920     1.860000e-01  2.390000e-01   0.000
Female44to54                                               -16.369     1.320000e-01  1.490000e-01   0.000
Male44to54                                                 -16.528     1.320000e-01  1.500000e-01   0.000
Median.Age                                                 -18.339     3.385000e+01  3.990300e+01   0.000
White                                                      -18.347     5.500200e+01  7.903500e+01   0.000
White..Asian                                               -18.738     5.612700e+01  8.010600e+01   0.000
lon                                                        -20.212    -1.078720e+02 -9.176500e+01   0.000
Summer.Prcp                                                -22.673     5.075190e+02  1.108740e+03   0.000
Autumn.Prcp                                                -23.144     3.929670e+02  9.303860e+02   0.000
precip                                                     -23.751     4.248130e+02  9.825080e+02   0.000
Spring.Prcp                                                -24.678     4.352590e+02  1.020002e+03   0.000
print(c("######### CLUSTER 5 ##########"))
[1] "######### CLUSTER 5 ##########"
round(fm.hcpc$desc.var$quanti[[5]][ ,c(1,2,3,6)],3)
                                                            v.test Mean in category  Overall mean p.value
RenterOccupied                                              28.206     4.420000e-01  2.770000e-01   0.000
Votes                                                       27.637     2.830042e+05  4.174833e+04   0.000
Total.Population                                            26.123     7.260978e+05  9.775404e+04   0.000
Asian                                                       25.549     5.501000e+00  1.071000e+00   0.000
Female20to24                                                24.410     1.040000e-01  5.700000e-02   0.000
Freq                                                        24.238     1.386900e+01  2.790000e+00   0.000
Precincts                                                   24.212     3.770380e+02  5.489400e+01   0.000
Male20to24                                                  23.380     1.130000e-01  6.300000e-02   0.000
Democrats.2016                                              22.855     5.866700e+01  3.169000e+01   0.000
Graduate.Degree                                             22.263     1.305100e+01  6.445000e+00   0.000
At.Least.Bachelors.s.Degree                                 20.543     3.268000e+01  1.899500e+01   0.000
HIV.prevalence.rate                                         19.595     4.557140e+02  1.491930e+02   0.000
CountyHU                                                    19.267     9.181546e+06  6.297451e+05   0.000
Democrats.2012                                              18.468     5.956300e+01  3.851200e+01   0.000
Democrats.2008                                              18.175     6.099400e+01  4.156800e+01   0.000
DemChange08                                                 17.971    -4.028000e+00 -2.615100e+01   0.000
DemChange12                                                 17.256    -1.496000e+00 -1.946300e+01   0.000
Violent.crime                                               16.210     5.054570e+02  2.514570e+02   0.000
MAR                                                         15.206     1.028000e+00  7.350000e-01   0.000
Female25to44                                                14.972     2.650000e-01  2.310000e-01   0.000
Management.professional.and.related.occupations             14.825     3.714200e+01  2.982700e+01   0.000
Sexually.transmitted.infections                             13.740     6.128050e+02  3.433310e+02   0.000
Gini.Coefficient                                            13.288     4.690000e-01  4.320000e-01   0.000
OtherRace                                                   12.733     3.342000e+00  1.582000e+00   0.000
Male25to44                                                  12.227     2.770000e-01  2.430000e-01   0.000
CFS                                                         11.987     0.000000e+00  0.000000e+00   0.000
Green.2016                                                  10.847     1.349000e+00  8.500000e-01   0.000
CountyArea                                                  10.780     3.562413e+10  1.200905e+10   0.000
Mixedness                                                    9.552     6.120000e-01 -1.000000e-02   0.000
Sales.and.office.occupations                                 9.300     2.532400e+01  2.284300e+01   0.000
Winter.Tmin                                                  8.579     3.073270e+02  2.404230e+02   0.000
Black                                                        8.224     1.797400e+01  8.830000e+00   0.000
Autumn.Tmin                                                  7.490     4.877250e+02  4.431530e+02   0.000
Winter.Tavg                                                  7.398     4.056490e+02  3.451100e+02   0.000
Annual.Tmin                                                  7.035     4.745310e+02  4.322210e+02   0.000
Max.Alc                                                      6.986     1.000000e-03  1.000000e-03   0.000
Children.in.single.parent.households                         6.928     3.710000e-01  3.160000e-01   0.000
Preschool.Enrollment.Ratio.enrolled.ages.3.and.4             6.819     5.055200e+01  4.298400e+01   0.000
Hispanic                                                     6.763     1.471100e+01  7.940000e+00   0.000
ACFS                                                         6.744     0.000000e+00  0.000000e+00   0.000
Spring.Tmin                                                  6.385     4.575320e+02  4.187250e+02   0.000
Autumn.Tavg                                                  6.300     5.978330e+02  5.615880e+02   0.000
Winter.Tmax                                                  6.176     5.034570e+02  4.490200e+02   0.000
temp                                                         5.839     1.453900e+01  1.262300e+01   0.000
Annual.Tavg                                                  5.839     5.817040e+02  5.472120e+02   0.000
School.Enrollment                                            5.681     7.726100e+01  7.498600e+01   0.000
TotalFemale                                                  5.475     5.100000e-01  5.010000e-01   0.000
Autumn.Tmax                                                  5.088     7.096940e+02  6.799320e+02   0.000
Spring.Tavg                                                  5.030     5.698450e+02  5.392730e+02   0.000
Annual.Tmax                                                  4.761     6.908190e+02  6.619730e+02   0.000
Winter.Prcp                                                  4.587     9.862030e+02  8.071530e+02   0.000
At.Least.High.School.Diploma                                 4.541     8.563800e+01  8.300900e+01   0.000
Service.occupations                                          4.459     1.865000e+01  1.744900e+01   0.000
Median.Earnings.2010                                         4.041     2.700585e+04  2.543765e+04   0.000
Summer.Tmin                                                  3.998     6.428390e+02  6.224800e+02   0.000
Spring.Tmax                                                  3.983     6.841960e+02  6.591830e+02   0.000
Summer.Tavg                                                  3.354     7.538580e+02  7.396110e+02   0.001
CropSales                                                    2.879     9.849885e+07  6.220057e+07   0.004
Libertarians.2016                                            2.852     3.497000e+00  3.163000e+00   0.004
Mean.Alc                                                     2.813     0.000000e+00  0.000000e+00   0.005
FruitNutSales                                                2.806     3.059535e+07  9.235039e+06   0.005
Poverty.Rate.below.federal.poverty.threshold                 2.735     1.682100e+01  1.547800e+01   0.006
S                                                            2.637    -1.560000e-01 -3.460000e-01   0.008
VeggieSales                                                  2.628     1.860103e+07  6.327674e+06   0.009
precip                                                       2.300     1.043938e+03  9.825080e+02   0.021
Summer.Tmax                                                  2.177     8.654270e+02  8.571330e+02   0.029
Unemployment                                                 2.072     8.100000e-02  7.700000e-02   0.038
Autumn.Prcp                                                  2.042     9.843310e+02  9.303860e+02   0.041
Amerindian                                                  -2.009     5.640000e-01  1.541000e+00   0.045
AgLandAcres                                                 -2.237     4.555692e+04  7.235322e+04   0.025
Children.Under.6.Living.in.Poverty                          -2.285     2.278400e+01  2.487600e+01   0.022
AnimalCV                                                    -2.528     1.120800e+01  1.256700e+01   0.011
FruitNutCV                                                  -2.633     2.097900e+01  2.496300e+01   0.008
AnimalSales                                                 -2.972     3.271525e+07  6.295049e+07   0.003
Poor.physical.health.days                                   -4.212     3.452000e+00  3.807000e+00   0.000
Adults.65.and.Older.Living.in.Poverty                       -4.261     9.735000e+00  1.152600e+01   0.000
CA                                                          -4.370    -3.220000e-01  1.000000e-02   0.000
CropCV                                                      -4.483     1.547200e+01  1.774700e+01   0.000
elevation                                                   -4.491     2.429790e+02  3.990520e+02   0.000
lat                                                         -5.338     3.624500e+01  3.825200e+01   0.000
Less.Than.High.School.Diploma                               -5.620     1.373700e+01  1.691100e+01   0.000
TempRange                                                   -5.730     2.162880e+02  2.297520e+02   0.000
Teen.births                                                 -6.099     3.476400e+01  4.408800e+01   0.000
Farming.fishing.and.forestry.occupations                    -7.792     5.550000e-01  2.110000e+00   0.000
Adult.smoking                                               -8.741     1.710000e-01  2.110000e-01   0.000
Diabetes                                                   -10.221     9.000000e-02  1.070000e-01   0.000
RepChange08                                                -10.256    -5.244000e+00  1.158200e+01   0.000
Injury.deaths                                              -10.651     5.712500e+01  7.575400e+01   0.000
Adult.obesity                                              -10.683     2.710000e-01  3.060000e-01   0.000
Female55to59                                               -11.273     6.100000e-02  7.000000e-02   0.000
White..Asian                                               -11.468     6.341000e+01  8.010600e+01   0.000
RepChange12                                                -11.749    -6.829000e+00  7.502000e+00   0.000
Male55to59                                                 -11.860     5.900000e-02  7.000000e-02   0.000
MarriedHouseholdRatio                                      -12.719     6.920000e-01  7.630000e-01   0.000
Construction.extraction.maintenance.and.repair.occupations -12.913     8.169000e+00  1.151900e+01   0.000
Production.transportation.and.material.moving.occupations  -13.187     1.016900e+01  1.625200e+01   0.000
Female44to54                                               -13.601     1.330000e-01  1.490000e-01   0.000
FemaleOver59                                               -13.747     1.840000e-01  2.390000e-01   0.000
White                                                      -14.176     5.790900e+01  7.903500e+01   0.000
Male44to54                                                 -14.386     1.330000e-01  1.500000e-01   0.000
MaleOver59                                                 -15.417     1.410000e-01  2.040000e-01   0.000
Median.Age                                                 -16.413     3.373900e+01  3.990300e+01   0.000
Sire.Homogeneity                                           -17.438     4.720000e-01  7.190000e-01   0.000
Republicans.2008                                           -17.876     3.771100e+01  5.678500e+01   0.000
Republicans.2012                                           -18.557     3.849300e+01  5.964100e+01   0.000
FamilyRatio                                                -19.865     6.010000e-01  6.780000e-01   0.000
Republicans.2016                                           -22.839     3.611700e+01  6.359700e+01   0.000
print(c("########## CLUSTER 6 ##########"))
[1] "########## CLUSTER 6 ##########"
round(fm.hcpc$desc.var$quanti[[6]][ ,c(1,2,3,6)],3)
                                                            v.test Mean in category  Overall mean p.value
Spring.Tavg                                                 31.187     6.079800e+02  5.392730e+02   0.000
temp                                                        30.844     1.629200e+01  1.262300e+01   0.000
Annual.Tavg                                                 30.844     6.132490e+02  5.472120e+02   0.000
Spring.Tmax                                                 30.754     7.291810e+02  6.591830e+02   0.000
Spring.Tmin                                                 30.623     4.861890e+02  4.187250e+02   0.000
Autumn.Tavg                                                 30.566     6.253350e+02  5.615880e+02   0.000
Autumn.Tmax                                                 30.484     7.445730e+02  6.799320e+02   0.000
Annual.Tmax                                                 30.349     7.286310e+02  6.619730e+02   0.000
Annual.Tmin                                                 30.218     4.980930e+02  4.322210e+02   0.000
Summer.Tavg                                                 29.731     7.853870e+02  7.396110e+02   0.000
Winter.Tmax                                                 29.443     5.430840e+02  4.490200e+02   0.000
Autumn.Tmin                                                 29.302     5.063620e+02  4.431530e+02   0.000
Winter.Tavg                                                 29.142     4.315530e+02  3.451100e+02   0.000
Summer.Tmin                                                 28.886     6.757990e+02  6.224800e+02   0.000
Winter.Tmin                                                 27.934     3.193820e+02  2.404230e+02   0.000
Summer.Tmax                                                 27.160     8.946330e+02  8.571330e+02   0.000
Republicans.2008                                            24.986     6.645000e+01  5.678500e+01   0.000
Republicans.2012                                            23.444     6.932400e+01  5.964100e+01   0.000
Poor.physical.health.days                                   22.617     4.499000e+00  3.807000e+00   0.000
Republicans.2016                                            22.604     7.345500e+01  6.359700e+01   0.000
Uninsured                                                   22.228     2.130000e-01  1.790000e-01   0.000
precip                                                      21.797     1.193568e+03  9.825080e+02   0.000
Less.Than.High.School.Diploma                               21.296     2.127000e+01  1.691100e+01   0.000
Diabetes                                                    20.943     1.200000e-01  1.070000e-01   0.000
Spring.Prcp                                                 20.710     1.222366e+03  1.020002e+03   0.000
Adult.smoking                                               20.396     2.440000e-01  2.110000e-01   0.000
Teen.births                                                 20.311     5.534300e+01  4.408800e+01   0.000
Autumn.Prcp                                                 20.205     1.123863e+03  9.303860e+02   0.000
Poor.mental.health.days                                     20.100     4.073000e+00  3.536000e+00   0.000
Winter.Prcp                                                 19.847     1.087941e+03  8.071530e+02   0.000
Construction.extraction.maintenance.and.repair.occupations  17.267     1.314200e+01  1.151900e+01   0.000
FruitNutCV                                                  17.046     3.431100e+01  2.496300e+01   0.000
Injury.deaths                                               15.526     8.559700e+01  7.575400e+01   0.000
FamilyRatio                                                 13.944     6.980000e-01  6.780000e-01   0.000
Summer.Prcp                                                 13.931     1.261076e+03  1.108740e+03   0.000
Adults.65.and.Older.Living.in.Poverty                       12.189     1.338200e+01  1.152600e+01   0.000
Child.Poverty.living.in.families.below.the.poverty.line     11.933     2.432800e+01  2.114500e+01   0.000
Low.birthweight                                             11.644     9.000000e-02  8.300000e-02   0.000
Poverty.Rate.below.federal.poverty.threshold                11.212     1.747300e+01  1.547800e+01   0.000
Adult.obesity                                               11.050     3.190000e-01  3.060000e-01   0.000
Children.Under.6.Living.in.Poverty                          10.824     2.846900e+01  2.487600e+01   0.000
Gini.Coefficient                                             8.871     4.410000e-01  4.320000e-01   0.000
Production.transportation.and.material.moving.occupations    8.792     1.772200e+01  1.625200e+01   0.000
lon                                                          7.945    -8.915400e+01 -9.176500e+01   0.000
CFS                                                          5.789     0.000000e+00  0.000000e+00   0.000
Children.in.single.parent.households                         5.445     3.310000e-01  3.160000e-01   0.000
Female25to44                                                 4.606     2.340000e-01  2.310000e-01   0.000
Male25to44                                                   4.542     2.470000e-01  2.430000e-01   0.000
Unemployment                                                 3.885     8.000000e-02  7.700000e-02   0.000
AveHousehouldSize                                            3.017     2.497000e+00  2.479000e+00   0.003
Violent.crime                                                2.992     2.684520e+02  2.514570e+02   0.003
AnimalCV                                                     2.813     1.311500e+01  1.256700e+01   0.005
FemaleOver59                                                 2.573     2.430000e-01  2.390000e-01   0.010
Black                                                        2.349     9.777000e+00  8.830000e+00   0.019
Sales.and.office.occupations                                 2.127     2.304900e+01  2.284300e+01   0.033
RepChange12                                                 -2.050     6.596000e+00  7.502000e+00   0.040
HIV.prevalence.rate                                         -2.112     1.372190e+02  1.491930e+02   0.035
RepChange08                                                 -2.114     1.032600e+01  1.158200e+01   0.035
VeggieSales                                                 -2.526     2.051556e+06  6.327674e+06   0.012
FruitNutSales                                               -2.889     1.263334e+06  9.235039e+06   0.004
CountyHU                                                    -3.179     1.182256e+05  6.297451e+05   0.001
Service.occupations                                         -3.433     1.711400e+01  1.744900e+01   0.001
AnimalSales                                                 -3.638     4.953442e+07  6.295049e+07   0.000
Female20to24                                                -3.652     5.500000e-02  5.700000e-02   0.000
VeggieCV                                                    -3.670     1.686200e+01  1.838500e+01   0.000
MarriedHouseholdRatio                                       -3.709     7.550000e-01  7.630000e-01   0.000
Male20to24                                                  -3.722     6.000000e-02  6.300000e-02   0.000
Female55to59                                                -3.926     6.900000e-02  7.000000e-02   0.000
Male44to54                                                  -3.927     1.480000e-01  1.500000e-01   0.000
DemChange08                                                 -4.031    -2.795000e+01 -2.615100e+01   0.000
Farming.fishing.and.forestry.occupations                    -4.120     1.812000e+00  2.110000e+00   0.000
MAR                                                         -4.322     7.040000e-01  7.350000e-01   0.000
HusbandWifeFamilyRatio                                      -4.921     2.720000e-01  2.790000e-01   0.000
CountyArea                                                  -5.093     7.964887e+09  1.200905e+10   0.000
Female44to54                                                -5.407     1.470000e-01  1.490000e-01   0.000
Total.Population                                            -5.740     4.770734e+04  9.775404e+04   0.000
Preschool.Enrollment.Ratio.enrolled.ages.3.and.4            -5.775     4.066000e+01  4.298400e+01   0.000
RenterOccupied                                              -6.222     2.630000e-01  2.770000e-01   0.000
CA                                                          -6.279    -1.630000e-01  1.000000e-02   0.000
Votes                                                       -6.478     2.125039e+04  4.174833e+04   0.000
Precincts                                                   -6.567     2.322400e+01  5.489400e+01   0.000
AgLandCV                                                    -6.656     2.123400e+01  2.381600e+01   0.000
Median.Earnings.2010                                        -7.201     2.442473e+04  2.543765e+04   0.000
Sire.Homogeneity                                            -7.687     6.800000e-01  7.190000e-01   0.000
Asian                                                       -7.703     5.860000e-01  1.071000e+00   0.000
CropSales                                                   -8.496     2.336845e+07  6.220057e+07   0.000
Male55to59                                                  -8.657     6.700000e-02  7.000000e-02   0.000
School.Enrollment                                           -8.944     7.368700e+01  7.498600e+01   0.000
Max.Alc                                                     -9.029     0.000000e+00  1.000000e-03   0.000
Mean.Alc                                                    -9.041     0.000000e+00  0.000000e+00   0.000
Freq                                                        -9.655     1.190000e+00  2.790000e+00   0.000
AgLandAcres                                                 -9.787     2.985184e+04  7.235322e+04   0.000
Mixedness                                                   -9.954    -2.450000e-01 -1.000000e-02   0.000
Graduate.Degree                                            -11.976     5.157000e+00  6.445000e+00   0.000
ACFS                                                       -12.224     0.000000e+00  0.000000e+00   0.000
elevation                                                  -13.596     2.277840e+02  3.990520e+02   0.000
Management.professional.and.related.occupations            -14.912     2.716000e+01  2.982700e+01   0.000
At.Least.Bachelors.s.Degree                                -15.428     1.527000e+01  1.899500e+01   0.000
Green.2016                                                 -18.331     5.440000e-01  8.500000e-01   0.000
S                                                          -18.541    -8.300000e-01 -3.460000e-01   0.000
Democrats.2016                                             -19.196     2.347700e+01  3.169000e+01   0.000
At.Least.High.School.Diploma                               -20.913     7.862000e+01  8.300900e+01   0.000
Libertarians.2016                                          -21.186     2.266000e+00  3.163000e+00   0.000
Democrats.2012                                             -22.361     2.927300e+01  3.851200e+01   0.000
Democrats.2008                                             -24.069     3.224300e+01  4.156800e+01   0.000
lat                                                        -29.582     3.422000e+01  3.825200e+01   0.000
print(c("########## CLUSTER 7 ##########"))
[1] "########## CLUSTER 7 ##########"
round(fm.hcpc$desc.var$quanti[[7]][ ,c(1,2,3,6)],3)
                                                            v.test Mean in category  Overall mean p.value
Black                                                       40.152     4.241200e+01  8.830000e+00   0.000
Low.birthweight                                             32.580     1.220000e-01  8.300000e-02   0.000
Sexually.transmitted.infections                             31.655     8.103660e+02  3.433310e+02   0.000
Children.in.single.parent.households                        31.445     5.040000e-01  3.160000e-01   0.000
Mean.Alc                                                    28.384     0.000000e+00  0.000000e+00   0.000
Child.Poverty.living.in.families.below.the.poverty.line     28.275     3.679900e+01  2.114500e+01   0.000
Poverty.Rate.below.federal.poverty.threshold                27.998     2.581700e+01  1.547800e+01   0.000
Adults.65.and.Older.Living.in.Poverty                       25.633     1.962900e+01  1.152600e+01   0.000
Children.Under.6.Living.in.Poverty                          25.586     4.250000e+01  2.487600e+01   0.000
Mixedness                                                   25.233     1.226000e+00 -1.000000e-02   0.000
Diabetes                                                    24.221     1.390000e-01  1.070000e-01   0.000
Adult.obesity                                               22.028     3.600000e-01  3.060000e-01   0.000
Less.Than.High.School.Diploma                               22.009     2.626100e+01  1.691100e+01   0.000
Teen.births                                                 21.879     6.924900e+01  4.408800e+01   0.000
ACFS                                                        21.254     0.000000e+00  0.000000e+00   0.000
Democrats.2016                                              21.230     5.054100e+01  3.169000e+01   0.000
Max.Alc                                                     20.057     1.000000e-03  1.000000e-03   0.000
HIV.prevalence.rate                                         19.650     3.804260e+02  1.491930e+02   0.000
Unemployment                                                19.641     1.080000e-01  7.700000e-02   0.000
DemChange08                                                 19.616    -7.984000e+00 -2.615100e+01   0.000
Democrats.2012                                              19.450     5.519100e+01  3.851200e+01   0.000
Spring.Tavg                                                 19.001     6.261530e+02  5.392730e+02   0.000
Spring.Tmin                                                 18.839     5.048650e+02  4.187250e+02   0.000
Annual.Tavg                                                 18.811     6.308000e+02  5.472120e+02   0.000
temp                                                        18.811     1.726700e+01  1.262300e+01   0.000
Autumn.Tavg                                                 18.617     6.421680e+02  5.615880e+02   0.000
Annual.Tmin                                                 18.584     5.163000e+02  4.322210e+02   0.000
Spring.Tmax                                                 18.477     7.464640e+02  6.591830e+02   0.000
Violent.crime                                               18.200     4.659950e+02  2.514570e+02   0.000
Autumn.Tmax                                                 18.191     7.599860e+02  6.799320e+02   0.000
Winter.Tavg                                                 18.187     4.570710e+02  3.451100e+02   0.000
Annual.Tmax                                                 18.127     7.446010e+02  6.619730e+02   0.000
Autumn.Tmin                                                 18.010     5.237820e+02  4.431530e+02   0.000
Winter.Tmax                                                 17.955     5.680710e+02  4.490200e+02   0.000
Winter.Tmin                                                 17.897     3.454130e+02  2.404230e+02   0.000
Gini.Coefficient                                            17.875     4.700000e-01  4.320000e-01   0.000
Summer.Tavg                                                 16.976     7.938590e+02  7.396110e+02   0.000
Summer.Tmin                                                 16.922     6.873070e+02  6.224800e+02   0.000
Democrats.2008                                              16.181     5.457800e+01  4.156800e+01   0.000
Winter.Prcp                                                 15.673     1.267344e+03  8.071530e+02   0.000
MAR                                                         15.270     9.560000e-01  7.350000e-01   0.000
Summer.Tmax                                                 14.829     8.996250e+02  8.571330e+02   0.000
precip                                                      14.356     1.271000e+03  9.825080e+02   0.000
DemChange12                                                 13.342    -9.013000e+00 -1.946300e+01   0.000
Service.occupations                                         12.211     1.992200e+01  1.744900e+01   0.000
Spring.Prcp                                                 11.048     1.244058e+03  1.020002e+03   0.000
Autumn.Prcp                                                 10.752     1.144061e+03  9.303860e+02   0.000
Summer.Prcp                                                 10.381     1.344324e+03  1.108740e+03   0.000
Uninsured                                                   10.358     2.120000e-01  1.790000e-01   0.000
VeggieCV                                                     9.614     2.666700e+01  1.838500e+01   0.000
Production.transportation.and.material.moving.occupations    8.995     1.937300e+01  1.625200e+01   0.000
RenterOccupied                                               8.359     3.140000e-01  2.770000e-01   0.000
Poor.physical.health.days                                    8.271     4.332000e+00  3.807000e+00   0.000
lon                                                          7.413    -8.670900e+01 -9.176500e+01   0.000
AnimalCV                                                     7.347     1.553800e+01  1.256700e+01   0.000
Preschool.Enrollment.Ratio.enrolled.ages.3.and.4             7.296     4.907600e+01  4.298400e+01   0.000
MaleUnder20                                                  6.673     2.840000e-01  2.700000e-01   0.000
AveHousehouldSize                                            6.307     2.555000e+00  2.479000e+00   0.000
Poor.mental.health.days                                      6.058     3.872000e+00  3.536000e+00   0.000
Injury.deaths                                                5.956     8.359100e+01  7.575400e+01   0.000
FemaleUnder20                                                5.731     2.650000e-01  2.540000e-01   0.000
Male25to44                                                   5.092     2.540000e-01  2.430000e-01   0.000
Adult.smoking                                                4.499     2.260000e-01  2.110000e-01   0.000
Male20to24                                                   4.425     7.000000e-02  6.300000e-02   0.000
CropCV                                                       3.869     1.922400e+01  1.774700e+01   0.000
Female20to24                                                 3.825     6.300000e-02  5.700000e-02   0.000
TotalFemale                                                  3.304     5.050000e-01  5.010000e-01   0.001
FruitNutCV                                                   3.225     2.863400e+01  2.496300e+01   0.001
RepChange12                                                  3.195     1.043400e+01  7.502000e+00   0.001
Farming.fishing.and.forestry.occupations                     2.439     2.477000e+00  2.110000e+00   0.015
Female25to44                                                 2.405     2.350000e-01  2.310000e-01   0.016
AgLandCV                                                     2.246     2.562400e+01  2.381600e+01   0.025
AgLandAcres                                                 -1.988     5.443904e+04  7.235322e+04   0.047
CropSales                                                   -2.229     4.106047e+07  6.220057e+07   0.026
Female55to59                                                -2.449     6.900000e-02  7.000000e-02   0.014
Construction.extraction.maintenance.and.repair.occupations  -2.554     1.102100e+01  1.151900e+01   0.011
Precincts                                                   -3.120     2.366900e+01  5.489400e+01   0.002
Total.Population                                            -3.201     3.982949e+04  9.775404e+04   0.001
Sales.and.office.occupations                                -3.263     2.218900e+01  2.284300e+01   0.001
AnimalSales                                                 -3.317     3.755941e+07  6.295049e+07   0.001
CountyArea                                                  -3.572     6.123291e+09  1.200905e+10   0.000
AveFamilySize                                               -3.918     2.887000e+00  2.923000e+00   0.000
Votes                                                       -4.071     1.501061e+04  4.174833e+04   0.000
Asian                                                       -4.387     4.980000e-01  1.071000e+00   0.000
FemaleOver59                                                -4.711     2.250000e-01  2.390000e-01   0.000
Male55to59                                                  -5.091     6.700000e-02  7.000000e-02   0.000
School.Enrollment                                           -5.156     7.343200e+01  7.498600e+01   0.000
Freq                                                        -5.516     8.930000e-01  2.790000e+00   0.000
Female44to54                                                -5.823     1.440000e-01  1.490000e-01   0.000
OtherRace                                                   -5.976     9.610000e-01  1.582000e+00   0.000
Male44to54                                                  -6.206     1.440000e-01  1.500000e-01   0.000
Median.Age                                                  -8.293     3.756000e+01  3.990300e+01   0.000
MaleOver59                                                  -8.678     1.770000e-01  2.040000e-01   0.000
Graduate.Degree                                             -8.720     4.499000e+00  6.445000e+00   0.000
Green.2016                                                 -10.192     4.970000e-01  8.500000e-01   0.000
elevation                                                  -11.287     1.039550e+02  3.990520e+02   0.000
At.Least.Bachelors.s.Degree                                -11.522     1.322100e+01  1.899500e+01   0.000
Median.Earnings.2010                                       -12.275     2.185399e+04  2.543765e+04   0.000
Management.professional.and.related.occupations            -12.957     2.501700e+01  2.982700e+01   0.000
Republicans.2008                                           -15.287     4.451300e+01  5.678500e+01   0.000
Republicans.2016                                           -17.769     4.751400e+01  6.359700e+01   0.000
lat                                                        -17.972     3.316800e+01  3.825200e+01   0.000
Republicans.2012                                           -18.352     4.390800e+01  5.964100e+01   0.000
Sire.Homogeneity                                           -19.627     5.100000e-01  7.190000e-01   0.000
HusbandWifeFamilyRatio                                     -20.457     2.190000e-01  2.790000e-01   0.000
At.Least.High.School.Diploma                               -21.282     7.373900e+01  8.300900e+01   0.000
Libertarians.2016                                          -21.856     1.241000e+00  3.163000e+00   0.000
CFS                                                        -22.359     0.000000e+00  0.000000e+00   0.000
CA                                                         -27.782    -1.579000e+00  1.000000e-02   0.000
White                                                      -29.278     4.621200e+01  7.903500e+01   0.000
White..Asian                                               -30.492     4.671000e+01  8.010600e+01   0.000
S                                                          -31.795    -2.068000e+00 -3.460000e-01   0.000
MarriedHouseholdRatio                                      -33.784     6.220000e-01  7.630000e-01   0.000

CLUSTER 1: Older male population, higher elevation, farming, conservative, large area, small houshold size, married (retired??)

CLUSTER 2: educated, married, more Trump over Obama, females 44-59, insured, healthy

CLUSTER 3: high income, educated, liberal, married, high ave. household size, employed, healthy

CLUSTER 4: hot, poor health, construction and transportation jobs, single parents, low income, republican

CLUSTER 5: Hispanic, young, farming counties, lower education levels,

CLUSTER 6: young, renters, educated, democrats, multi-cultural,

CLUSTER 7: democrats, poor health, low education, renters, low family size

The number of farmers markets per county is positively correlated with clusters 3 and 5, and negatively correlated with clusters 1, 2, 4, 5, and 7.

Assign cluster and PCA coordinates to final data set.

All_Final_Data_Cleaned2 = cbind(All_Final_Data_Cleaned[ ,1:2], fm.hcpc$data.clust, fm.pca$ind$coord) %>% mutate_if(is.factor, as.character)
write.csv(All_Final_Data_Cleaned2, "Cleaned Data/All_Final_Data_Cleaned2.csv")

Oh lets map the clusters.

Let’s add the geography to create a shape file.

G2 = left_join(G1,  All_Final_Data_Cleaned2, by = "RowName")
tm_view()
$tm_layout
$tm_layout$style
[1] NA


attr(,"class")
[1] "tm"
tm_shape(G2) + tm_fill("clust",  palette = "Set1") + 
  tm_layout(legend.position = c(.87,.25),
            legend.text.size = .5, 
            title.position = c("right","bottom"),
            title = "Cluster",
            title.fontface = "bold" ,
            title.size = 1) 

Modeling and Analysis

All_Final_Data_Cleaned2 %>% ggplot(aes(x = clust, y = Freq, color = clust)) + geom_point(aes(color = clust))  +
There were 28 warnings (use warnings() to see them)
    scale_fill_brewer(palette="Set1") + labs(title="Farmers Markets by Cluster", face = "bold",x="Cluster", y = "Length") + theme_minimal()

Modeling and Analysis

In this section, we will first explore whether certain variables, whose domains have been deemed signiificant in the past, have a significant effect on the frequency of farmers markets per county, while controling for certain variables.

Since the response variable is a count data, we will explore Poisson, quasipoisson, and negative binomials. Zero inflated models will be investigated but the algorithms tend to fail to converge as the models became slightly more complex. Each variable or combination of variables will be tested in each of the aforementioned models, and the model with the lowest AIC will be selected. AIC is preferred for this study since it is penalized by the addition of more parameters. The selected model will be tested using a goodness of fit statistic.

A data frame of each model’s predicted value, as well the chi-squared contribution, will be created to ultimately assess which counties deviate most from what is expected under the assumptions of te given model.

Model 1: log(Total.Population)

We will use a log transformation on the popultion variable.

m1.pois = glm(Freq~ log(Total.Population), data = All_Final_Data_Cleaned2, family  = "poisson")
m1.nb = glm.nb(Freq~ log(Total.Population), data = All_Final_Data_Cleaned2)
m1.pois0 = zeroinfl(m1.pois, dist = "poisson")
m1.nb0 = zeroinfl(m1.pois, dist = "negbin")
L1 = list(m1.pois, m1.nb, m1.pois0, m1.nb0)
print(rbind(c("Poisson", "NegBin", "ZeroInflPoisson", "ZeroInflNegBin"),AIC = round(sapply( L1, AIC),2)))
    [,1]       [,2]       [,3]              [,4]            
    "Poisson"  "NegBin"   "ZeroInflPoisson" "ZeroInflNegBin"
AIC "11273.54" "10464.35" "11276.6"         "10468.35"      

The negative binimial model is the preferred model.

summary(m1.nb)

Call:
glm.nb(formula = Freq ~ log(Total.Population), data = All_Final_Data_Cleaned2, 
    init.theta = 4.563667704, link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.7401  -0.9777  -0.2650   0.4209   4.1685  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           -6.77504    0.12238  -55.36   <2e-16 ***
log(Total.Population)  0.70134    0.01085   64.66   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(4.5637) family taken to be 1)

    Null deviance: 9110.4  on 3113  degrees of freedom
Residual deviance: 3077.0  on 3112  degrees of freedom
AIC: 10464

Number of Fisher Scoring iterations: 1

              Theta:  4.564 
          Std. Err.:  0.324 

 2 x log-likelihood:  -10458.354 
plot(m1.nb)

m1 = m1.nb

Model 2: Family size

Studies suggest that females 44-59 represent the largest demographic of shoppers at farmers markets, citing many are purchasing fresh produce o cook for their family. Thus we lookat average family size, controlling for population.

m2.pois = glm(Freq~ log(Total.Population) + AveFamilySize, data = All_Final_Data_Cleaned2, family  = "poisson")
m2.nb = glm.nb(Freq~ log(Total.Population) + AveFamilySize, data = All_Final_Data_Cleaned2)
m2.pois0 = zeroinfl(m2.pois, dist = "poisson")
m2.nb0 = zeroinfl(m2.pois, dist = "negbin")
L2 = list(m2.pois, m2.nb, m2.pois0, m2.nb0)
rbind(c("Poisson", "NegBin", "ZeroInflPoisson", "ZeroInflNegBin"),AIC = round(sapply( L2, AIC),2))
    [,1]       [,2]       [,3]              [,4]            
    "Poisson"  "NegBin"   "ZeroInflPoisson" "ZeroInflNegBin"
AIC "10988.61" "10299.54" "10990"           "10302.16"      

Again, the negative binimial model is the preferred model.

summary(m2.nb)

Call:
glm.nb(formula = Freq ~ log(Total.Population) + AveFamilySize, 
    data = All_Final_Data_Cleaned2, init.theta = 5.25030653, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8040  -0.9503  -0.2584   0.4492   4.0972  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           -3.25374    0.29960  -10.86   <2e-16 ***
log(Total.Population)  0.75422    0.01137   66.32   <2e-16 ***
AveFamilySize         -1.39837    0.10969  -12.75   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(5.2503) family taken to be 1)

    Null deviance: 9587.5  on 3113  degrees of freedom
Residual deviance: 3021.8  on 3111  degrees of freedom
AIC: 10300

Number of Fisher Scoring iterations: 1

              Theta:  5.250 
          Std. Err.:  0.394 

 2 x log-likelihood:  -10291.539 
plot(m2.nb)

m2 = m2.nb

PCA

Now lets see how the coordinates of the PCA pan out. We’ll use the first 18 dimensions from the PCA, enough to account for 80% of variation between the dimensions. We will start by using all dimensions to select a distribution. Then we will eliminate non-significant dimensions one at a time, removing the dimension with the highest p-value.

m3.pois = glm(Freq~ ., data = All_Final_Data_Cleaned2 %>% dplyr::select(Freq, Dim.1:Dim.18), family  = "poisson")
m3.nb = glm.nb(Freq~ ., data = All_Final_Data_Cleaned2 %>% dplyr::select(Freq, Dim.1:Dim.18))
alternation limit reached
m3.pois0 = zeroinfl(m3.pois, dist = "poisson")
glm.fit: fitted probabilities numerically 0 or 1 occurred
m3.nb0 = zeroinfl(m3.pois, dist = "negbin")
glm.fit: fitted probabilities numerically 0 or 1 occurred
L3 = list(m3.pois, m3.nb, m3.pois0, m3.nb0)
rbind(c("Poisson", "NegBin", "ZeroInflPoisson", "ZeroInflNegBin"),AIC = round(sapply( L3, AIC),2))
    [,1]      [,2]       [,3]              [,4]            
    "Poisson" "NegBin"   "ZeroInflPoisson" "ZeroInflNegBin"
AIC "11425.9" "10402.19" "11244.8"         "10341.26"      

Again, the negative binimial model is the preferred model.

summary(m3.nb0)

Call:
zeroinfl(formula = m3.pois, dist = "negbin")

Pearson residuals:
    Min      1Q  Median      3Q     Max 
-2.1401 -0.6723 -0.1954  0.4338 19.8183 

Count model coefficients (negbin with log link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  0.511824   0.020870  24.524  < 2e-16 ***
Dim.1       -0.032506   0.003980  -8.167 3.17e-16 ***
Dim.2        0.186849   0.004112  45.438  < 2e-16 ***
Dim.3       -0.006527   0.006179  -1.056 0.290860    
Dim.4        0.138626   0.006366  21.774  < 2e-16 ***
Dim.5        0.013837   0.007325   1.889 0.058881 .  
Dim.6       -0.162546   0.008097 -20.074  < 2e-16 ***
Dim.7        0.032420   0.007392   4.386 1.16e-05 ***
Dim.8        0.029352   0.009593   3.060 0.002216 ** 
Dim.9       -0.034286   0.009765  -3.511 0.000446 ***
Dim.10       0.023687   0.011341   2.089 0.036737 *  
Dim.11       0.076589   0.013406   5.713 1.11e-08 ***
Dim.12       0.010834   0.013557   0.799 0.424211    
Dim.13      -0.030414   0.014151  -2.149 0.031613 *  
Dim.14       0.019759   0.013461   1.468 0.142160    
Dim.15       0.039606   0.016165   2.450 0.014279 *  
Dim.16       0.031493   0.015454   2.038 0.041570 *  
Dim.17      -0.021219   0.015328  -1.384 0.166242    
Dim.18      -0.074528   0.014847  -5.020 5.18e-07 ***
Log(theta)   1.554344   0.068730  22.615  < 2e-16 ***

Zero-inflation model coefficients (binomial with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -9.6404     1.6937  -5.692 1.26e-08 ***
Dim.1         0.2463     0.1176   2.094 0.036233 *  
Dim.2        -2.1906     0.4515  -4.852 1.22e-06 ***
Dim.3         0.1929     0.1410   1.368 0.171403    
Dim.4        -0.6238     0.1569  -3.977 6.98e-05 ***
Dim.5        -0.6823     0.2397  -2.847 0.004419 ** 
Dim.6         3.3647     0.6462   5.207 1.92e-07 ***
Dim.7        -2.4141     0.5909  -4.086 4.39e-05 ***
Dim.8        -4.5314     0.9892  -4.581 4.62e-06 ***
Dim.9        -1.9116     0.4908  -3.895 9.84e-05 ***
Dim.10        2.0023     0.5575   3.592 0.000328 ***
Dim.11       -0.4645     0.1846  -2.516 0.011853 *  
Dim.12        0.3165     0.2231   1.419 0.155980    
Dim.13        0.5409     0.2115   2.557 0.010549 *  
Dim.14       -0.1144     0.1773  -0.645 0.518881    
Dim.15        0.9210     0.3191   2.886 0.003898 ** 
Dim.16        0.3528     0.2947   1.197 0.231312    
Dim.17        0.6595     0.3428   1.924 0.054370 .  
Dim.18       -0.6653     0.3234  -2.057 0.039680 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Theta = 4.732 
Number of iterations in BFGS optimization: 79 
Log-likelihood: -5132 on 39 Df
plot(m3.nb0)
Error in xy.coords(x, y, xlabel, ylabel, log) : 
  'x' is a list, but does not have components 'x' and 'y'

We can use backwards elimination to reduce dimesnsion.

m4.nb0 = be.zeroinfl(m3.nb0, All_Final_Data_Cleaned2, dist = "negbin")
glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
summary(m4.nb0)

Call:
zeroinfl(formula = eval(parse(text = out)), data = data, dist = dist)

Pearson residuals:
    Min      1Q  Median      3Q     Max 
-2.1336 -0.6998 -0.1868  0.4376 41.7427 

Count model coefficients (negbin with log link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  0.492689   0.020633  23.879  < 2e-16 ***
Dim.1       -0.038999   0.003597 -10.843  < 2e-16 ***
Dim.2        0.187890   0.003989  47.101  < 2e-16 ***
Dim.4        0.140961   0.006277  22.456  < 2e-16 ***
Dim.5        0.019696   0.007008   2.811 0.004945 ** 
Dim.6       -0.168274   0.008009 -21.010  < 2e-16 ***
Dim.7        0.025218   0.007330   3.440 0.000581 ***
Dim.8        0.028972   0.009563   3.029 0.002450 ** 
Dim.9       -0.027213   0.009564  -2.845 0.004435 ** 
Dim.10       0.022532   0.011012   2.046 0.040748 *  
Dim.11       0.083064   0.013081   6.350 2.15e-10 ***
Dim.13      -0.044514   0.013383  -3.326 0.000880 ***
Dim.15       0.055444   0.015776   3.514 0.000441 ***
Dim.16       0.049225   0.014690   3.351 0.000806 ***
Dim.18      -0.075191   0.014575  -5.159 2.48e-07 ***
Log(theta)   1.546323   0.070232  22.017  < 2e-16 ***

Zero-inflation model coefficients (binomial with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -7.9194     1.7067  -4.640 3.48e-06 ***
Dim.2        -1.1119     0.2571  -4.325 1.53e-05 ***
Dim.4        -0.3994     0.1185  -3.372 0.000747 ***
Dim.6         1.8369     0.3608   5.091 3.56e-07 ***
Dim.7        -1.6013     0.3634  -4.406 1.05e-05 ***
Dim.8        -2.4572     0.5199  -4.727 2.28e-06 ***
Dim.9        -0.8262     0.3069  -2.692 0.007098 ** 
Dim.10        0.6139     0.2319   2.647 0.008117 ** 
Dim.11       -0.9988     0.2710  -3.686 0.000228 ***
Dim.15        1.1735     0.3681   3.188 0.001433 ** 
Dim.16        1.0770     0.3667   2.937 0.003316 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Theta = 4.6942 
Number of iterations in BFGS optimization: 55 
Log-likelihood: -5151 on 27 Df
AIC(m4.nb0)
[1] 10356.31
m4 = m4.nb0

Analyst’s Choice

The PCA analysis from before helps shed light on which variables are independent. Furthermore, the previous research into customer profiles helps shed light on which variables give us reasonable information. With this in mind, I set out to find some combination of variables that include measures of county population, political preference, family relations, age distribution, income, agricultural data, health, home ownership demographics, and eduacation. Each variable or variables were selected to reduce AIC in the model while remaining as a significant predictor. Extreme caution should be used in interpreting the results.

We can see the if the correlation between the predictors.

M = All_Final_Data_Cleaned2 %>% dplyr::select(Freq, Total.Population,Green.2016  , Median.Earnings.2010 , FruitNutCV   ,Median.Age , RenterOccupied , Poor.physical.health.days ,Annual.Tavg , precip , Graduate.Degree)
corrplot(cor(M))

Although Graduate degree appears to be colinear with other factors, I havve chosen to let it remain in the model since it does tend to lower the AIC in the models I have experimented with and PCA has shown it to be a strong representation in the lower dimensons of the PCA.

m5.pois = glm(Freq~  log(Total.Population)  + Green.2016  + Median.Earnings.2010 + FruitNutCV  + Median.Age + RenterOccupied + Poor.physical.health.days + Annual.Tavg + precip +  Graduate.Degree , data = All_Final_Data_Cleaned2, family  = "poisson")
m5.nb = glm.nb(Freq ~  log(Total.Population)  + Green.2016  + Median.Earnings.2010 + FruitNutCV  + Median.Age + RenterOccupied + Poor.physical.health.days + Annual.Tavg + precip +  Graduate.Degree , data = All_Final_Data_Reduced )
L5 = list(m5.pois, m5.nb)
rbind(c("Poisson", "NegBin"),AIC = round(sapply( L5, AIC),2))
    [,1]      [,2]     
    "Poisson" "NegBin" 
AIC "9600.41" "9447.58"
summary(m5.nb)

Call:
glm.nb(formula = Freq ~ log(Total.Population) + Green.2016 + 
    Median.Earnings.2010 + FruitNutCV + Median.Age + RenterOccupied + 
    Poor.physical.health.days + Annual.Tavg + precip + Graduate.Degree, 
    data = All_Final_Data_Reduced, init.theta = 16.04581749, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.7886  -0.9084  -0.1959   0.4780   4.5338  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -6.762e+00  2.381e-01 -28.398  < 2e-16 ***
log(Total.Population)      7.003e-01  1.284e-02  54.540  < 2e-16 ***
Green.2016                 2.233e-01  2.156e-02  10.361  < 2e-16 ***
Median.Earnings.2010      -1.331e-05  3.114e-06  -4.273 1.93e-05 ***
FruitNutCV                -5.302e-03  9.122e-04  -5.812 6.17e-09 ***
Median.Age                 4.379e-02  3.663e-03  11.955  < 2e-16 ***
RenterOccupied             1.141e+00  2.045e-01   5.580 2.41e-08 ***
Poor.physical.health.days -9.581e-02  1.828e-02  -5.241 1.59e-07 ***
Annual.Tavg               -3.326e-03  2.231e-04 -14.908  < 2e-16 ***
precip                     1.568e-04  4.533e-05   3.458 0.000544 ***
Graduate.Degree            2.290e-02  3.957e-03   5.786 7.19e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(16.0458) family taken to be 1)

    Null deviance: 12964.3  on 3113  degrees of freedom
Residual deviance:  2757.6  on 3103  degrees of freedom
AIC: 9447.6

Number of Fisher Scoring iterations: 1

              Theta:  16.05 
          Std. Err.:  2.03 

 2 x log-likelihood:  -9423.584 
plot(m5.nb)

m5 = m5.nb0

Goodness of Fit. Here we test H0: The observed farmers market frequencies follow the given negative binomial model vs. H1: The observed farmers market frequencies do not follow the given negative binomial model using a chi-squared goodness of fit with N - p = 3114 - 11 = 3103 df.

qchisq(.95, 3103)
[1] 3233.706

The 5% critical value is 3233.706, which is greater than the observed deviance of 2757.6. So we fail to reject the null and can conclude that the given negative binomial model does fit the observed data.

Although it is possible to reduce AIC further, this model is can help us make some general interpretations about the frequency

Lastly we can use zero inflated model with backwards selection with the remaining variables to reduce AIC to build a model with the highest predictive power.

m5.nb0 = zeroinfl(m5.nb, data = All_Final_Data_Cleaned2, dist = "negbin")
m6 = be.zeroinfl(m5.nb0,data = All_Final_Data_Cleaned2, dist = "negbin")
NaNs producedNaNs producedNaNs produced
AIC(m5.nb)
[1] 9447.584
AIC(m6)
[1] 9407.916
summary(m6)
NaNs produced

Call:
zeroinfl(formula = eval(parse(text = out)), data = data, dist = dist)

Pearson residuals:
    Min      1Q  Median      3Q     Max 
-2.0917 -0.6741 -0.1406  0.4980  8.5273 

Count model coefficients (negbin with log link):
                            Estimate Std. Error  z value Pr(>|z|)    
(Intercept)               -6.744e+00  2.396e-01  -28.150  < 2e-16 ***
log(Total.Population)      6.883e-01  1.122e-02   61.329  < 2e-16 ***
Green.2016                 2.242e-01  2.159e-02   10.384  < 2e-16 ***
Median.Earnings.2010      -1.240e-05  3.546e-08 -349.630  < 2e-16 ***
FruitNutCV                -4.187e-03  9.478e-04   -4.417 1.00e-05 ***
Median.Age                 4.378e-02  3.666e-03   11.944  < 2e-16 ***
RenterOccupied             1.178e+00  1.954e-01    6.028 1.66e-09 ***
Poor.physical.health.days -1.043e-01  1.825e-02   -5.714 1.11e-08 ***
Annual.Tavg               -3.088e-03  2.278e-04  -13.558  < 2e-16 ***
precip                     1.407e-04  4.673e-05    3.011   0.0026 ** 
Graduate.Degree            2.173e-02  3.640e-03    5.970 2.38e-09 ***
Log(theta)                 2.786e+00  4.545e-03  612.997  < 2e-16 ***

Zero-inflation model coefficients (binomial with logit link):
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -2.300e+00  5.983e+00  -0.384 0.700698    
log(Total.Population)     -2.403e+00  4.267e-01  -5.633 1.78e-08 ***
Median.Earnings.2010       7.312e-05         NA      NA       NA    
FruitNutCV                 6.164e-02  1.616e-02   3.813 0.000137 ***
Poor.physical.health.days -1.570e+00  4.193e-01  -3.745 0.000180 ***
Annual.Tavg                4.691e-02  8.062e-03   5.819 5.93e-09 ***
Graduate.Degree           -1.114e+00  2.668e-01  -4.177 2.96e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Theta = 16.2177 
Number of iterations in BFGS optimization: 69 
Log-likelihood: -4685 on 19 Df

Comparing Estimates

We’ll make a data set containing the observed frequncy and the expected frequency under the last four models. Models 1 and 2 are too simple and their AIC is too high for final consideration. We construct the chi-squared component of each observation and model, and record whether the residual is possitive or negative.

Expected = data.frame(cbind(All_Final_Data_Reduced$Freq, m3$fitted.values, m4$fitted.values,
                 m5$fitted.values, m6$fitted.values, m3$residuals, m4$residuals, m5$residuals, m6$residuals )) %>% 
                rename(Freq = X1, ExpectedMod3 = X2, ExpectedMod4 = X3, ExpectedMod5 = X4, ExpectedMod6 = X5, 
                       Resid3 = X6, Resid4 = X7, Resid5 = X8, Resid6 = X9) %>% mutate(Component3 = (Resid3^2)/ExpectedMod3,
                                                                                      Component4 = (Resid4^2)/ExpectedMod4,
                                                                                      Component5 = (Resid5^2)/ExpectedMod5,
                                                                                      Component6 = (Resid6^2)/ExpectedMod6)   %>%
                                                                mutate(Component3 = ifelse(Resid3 < 0, -Component3 , Component3),
                                                                       Component4 = ifelse(Resid4 < 0, -Component4 , Component4),
                                                                       Component5 = ifelse(Resid5 < 0, -Component5 , Component5),
                                                                       Component6 = ifelse(Resid6 < 0, -Component6 , Component6))
rownames(Expected) = rownames(All_Final_Data_Cleaned2)
Expected = round(Expected, 4) %>% arrange(Component6)
head(Expected)

The data has been sorted in descending terms of the “negative” chi squared contributions for model 6, which has the lowest AIC. Models 3 and 4 use the PCA components and the model has strange behavior when handling outliers. Three major outliers were identified, Los Angeles County, CA and Cook County, IL have very large populations, and Shannon County, SD has a very high Native American population. Thus the PCA models should be interpreted with caution, especially when dealing with counties with exetreme observations.

Queens.County.NY, Richmond.County.NY, and Arapahoe.County.CO are the three leading candidates that show promise for holding more farmers markets. Queens has only 19 farmers markets when the models 5 and 6 predict anywhere from about 46 farmers markets. Arapahoe County, CO, containing portions of suburban Denver, has only 4 markets where the models predict somewhere between 9 and 14.

---
title: "Farmers Market Analysis R Notebook"
output:
  html_document:
    df_print: paged
---

This document walks through the code used to obtain the results of my project. The section include:

Data Cleaning
Visualizations
PCA and Hierarchical CLustering
Model Building and Diagnostics

## Loading Packages

The R commands for this project will be supplied as needed. First, the following libraries and functions will need to be loaded.
```{r}
# rm(list = ls())
setwd("/Volumes/Macintosh HD - Data/SCHOOL/WGU/CapstoneProject")  ##set your path
library(tidycensus)
library(dplyr)
library(tidyverse)
library(tigris)
library(leaflet)
library(stringr)
library(sf)
library(purrr)
library(zipcode)
library(stringi)
library(ggplot2)
library(devtools)
library(tmap)         
library(tmaptools)  
library(FactoMineR)
library(tm)
library(stats)
library(openintro)
library(missMDA)
library(pscl)
library(factoextra)
library(openintro)
library(missMDA)
library(devtools)
library(PerformanceAnalytics)
library(ggrepel)
library(scales)
library(conflicted)
library(ResourceSelection)
library(caret)
library(MASS)
library(mpath)
library(DataExplorer)
library(corrplot)
library(knitr)

## set conflicts
conflict_prefer("select", "dplyr")
conflict_prefer("filter", "dplyr")

## custom functions
medianWithoutNA = function(x) {
  median(x[which(!is.na(x))])
}
add_cols <- function(.data, ..., .f = sum){
  tmp <- dplyr::select_at(.data, dplyr::vars(...))
  purrr::pmap_dbl(tmp, .f = .f)
} ## great function to sum up multiple columns from https://github.com/tidyverse/dplyr/issues/4544
```

## Data Cleaning

The farmers market, election, USDA, and zip code data files are uploaded.  Because most of these files use state and county codes, such as "003," we set the appropriate columns to character columns and add leading 0's where necessary.  Additionally we are adding the FIPS code to the USDA data, which is a concatonation of the sate and county codes.

The farmers market data can be found here: https://www.kaggle.com/madeleineferguson/farmers-markets-in-the-united-states?select=farmers_markets_from_usda.csv

The election data set (which is awesome btw) can be found at: https://public.opendatasoft.com/explore/dataset/usa-2016-presidential-election-by-county/information/?disjunctive.state&refine.state=Texas&dataChart=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%3D%3D&basemap=mapbox.light&location=5,38.73695,-100.08545

The USDA data was obtained using the QuickStat filters at: https://quickstats.nass.usda.gov/
Data was collected at the county level for te 2017 estimates.
```{r}
farmers_markets = read.csv("Raw Data/farmers_markets_from_usda.csv") %>% mutate_if(is.factor, as.character) 

Election2016 = read.csv("Raw Data/Election2016byCounty.csv", 
                        colClasses = c(Fips = "character")) %>% mutate_if(is.factor, as.character) 

zips_in_county_subdiv = read.table("Raw Data/Zip Code Referential Table.txt", header = TRUE, sep = ",",
                         colClasses = c(ZCTA5 = "character", 
                         STATE =    "character",  COUNTY = "character", COUSUB = "character",  
                         GEOID  = "character", CLASSFP = "character"))
animal_total_sales = read.csv("Raw Data/USDA DATA/AnimalTotalByCounty.csv",
                        colClasses = c(State.ANSI = "character", County.ANSI = "character" )) %>%
  mutate(State.ANSI = ifelse(nchar(State.ANSI) == 1, paste0("0", State.ANSI), State.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 1, paste0("0", County.ANSI), County.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 2, paste0("0", County.ANSI), County.ANSI),
         Fips = paste0(State.ANSI,County.ANSI))

crop_total_sales = read.csv("Raw Data/USDA DATA/CropTotalsSalesDollars.csv",
          colClasses = c(State.ANSI = "character", County.ANSI = "character" )) %>%
  mutate(State.ANSI = ifelse(nchar(State.ANSI) == 1, paste0("0", State.ANSI), State.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 1, paste0("0", County.ANSI), County.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 2, paste0("0", County.ANSI), County.ANSI),
         Fips = paste0(State.ANSI,County.ANSI))

fruit_and_nuts_total_sales = read.csv("Raw Data/USDA DATA/FruitNutsSAlesDollars.csv",
          colClasses = c(State.ANSI = "character", County.ANSI = "character" )) %>%
  mutate(State.ANSI = ifelse(nchar(State.ANSI) == 1, paste0("0", State.ANSI), State.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 1, paste0("0", County.ANSI), County.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 2, paste0("0", County.ANSI), County.ANSI),
         Fips = paste0(State.ANSI,County.ANSI))

veggie_total_sales = read.csv("Raw Data/USDA DATA/VeggieTotalSalesDollars.csv",
          colClasses = c(State.ANSI = "character", County.ANSI = "character" )) %>%
  mutate(State.ANSI = ifelse(nchar(State.ANSI) == 1, paste0("0", State.ANSI), State.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 1, paste0("0", County.ANSI), County.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 2, paste0("0", County.ANSI), County.ANSI),
         Fips = paste0(State.ANSI,County.ANSI))

AG_land = read.csv("Raw Data/USDA DATA/AGLand.csv",
           colClasses = c(State.ANSI = "character", County.ANSI = "character" )) %>%
  mutate(State.ANSI = ifelse(nchar(State.ANSI) == 1, paste0("0", State.ANSI), State.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 1, paste0("0", County.ANSI), County.ANSI),
         County.ANSI = ifelse(nchar(County.ANSI) == 2, paste0("0", County.ANSI), County.ANSI),
         Fips = paste0(State.ANSI,County.ANSI))
```

The goal will be to create a frequency table with the farmers market data with the number of farmers markets per county, along with cleaning and matching incomplete entries.  The USDA data can be combined into one data set.  The zip code data will be used as a reference, assigning a given zip code to the county where the largest portion of residents live (zip codes can span more than one county).  The election data set is complete.  All of these tables will be joined by using the FIPS code as a primary key.

# USDA Data Joining
FIPS codes will be used to join all six tables, which contain the values of the given statistic along with its CV.  Alaska will not be included.

Next the six tables are joined together using the Fips code as a primary key.  Alaska, state code 02, is being fitered out. 
```{r}
U1 = animal_total_sales %>% dplyr::select(Fips, State, County, State.ANSI,County.ANSI, Ag.District, Value, CV....) %>% rename( AnimalSales = Value, AnimalCV = CV....) %>% filter(State.ANSI != "02") %>% left_join(crop_total_sales %>% filter(Year == 2017) %>% select(Fips, Value, CV....)   %>% rename(CropSales = Value, CropCV = CV....), by = "Fips") %>%  left_join(fruit_and_nuts_total_sales %>% filter(Year == 2017) %>% select(Fips, Value, CV....)   %>% rename(FruitNutSales = Value, FruitNutCV = CV....), by = "Fips")%>%  left_join(veggie_total_sales %>% filter(Year == 2017) %>% select(Fips, Value, CV....)   %>% rename(VeggieSales = Value, VeggieCV = CV....), by = "Fips") %>% left_join(AG_land %>% filter(Year == 2017) %>% select(Fips, Value, CV....)   %>% rename(AgLandAcres = Value, AgLandCV = CV....), by = "Fips") %>% filter(!State == "02")

head(U1)
str(U1)
```

```{r}
plot_missing(U1)
```


Using the NASS glossary, the code (D) is for disclosed.  (H) refers to high CV value, over 99.95%, and (Z) refers to almost 0. This coding is causing numeric variables to be interpreted as a character.  Let's see what's missing.
```{r}
tot_na1 = apply(U1, 2, function(x) length(which(is.na(x))))
tot_na1[tot_na1 > 0]
```

The missing or disclosed numeric variables are removed and the columns are formatted to be numeric variables. Drop FruitNutSales, FruitNutCV, VeggieSales, and VeggieCV due to missingness.
```{r}
## (D) = disclosed, (H) = high, 99.95%+, (Z) = almost 0 (from NASS glossary)
## remove commas, change to numeric variables
w = 7:16 ## column indices of variables to be imputed
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, trimws))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, function(y) gsub("(D)", NA, y)))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, function(y) gsub("(Z)", "0", y)))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, function(y) gsub("(H)", "99.95", y)))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, function(y) gsub(",", "", y)))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, as.numeric))
U1 = cbind(U1[ ,-w], apply(U1[ ,w], 2, as.numeric))
```
Missing and disclosed values will be imputed by using the agricultural district medians.  Drop 
```{r}
U2 = U1 %>%  group_by(Ag.District) %>% filter(!is.na(State)) %>% 
             mutate(AnimalSales = ifelse(is.na(AnimalSales), medianWithoutNA(AnimalSales),
                                        AnimalSales),
             AnimalCV = ifelse(is.na(AnimalCV), medianWithoutNA(AnimalCV), AnimalCV),
             FruitNutSales  = ifelse(is.na(FruitNutSales ), medianWithoutNA(FruitNutSales ),
                                     FruitNutSales ),
             FruitNutCV  = ifelse(is.na(FruitNutCV), medianWithoutNA(FruitNutCV), FruitNutCV),
             VeggieSales  = ifelse(is.na(VeggieSales), medianWithoutNA(VeggieSales),
                                   VeggieSales),
            VeggieCV  = ifelse(is.na(VeggieCV), medianWithoutNA(VeggieCV), VeggieCV),
            CropSales = ifelse(is.na(CropSales), medianWithoutNA(CropSales), CropSales),
            CropCV  = ifelse(is.na(CropCV ), medianWithoutNA(CropCV ), CropCV ),
            AgLandAcres = ifelse(is.na(AgLandAcres), medianWithoutNA(AgLandAcres), AgLandAcres),
            AgLandCV = ifelse(is.na(AgLandCV), medianWithoutNA(AgLandCV), AgLandCV)
                   ) %>% ungroup()
```


```{r}
tot_na2 = apply(U2, 2, function(x) length(which(is.na(x))))
tot_na2
```

The remaining NA's will be imputed using state medians.
```{r}
U3 = U2 %>%  group_by(State) %>%
  mutate(AnimalSales = ifelse(is.na(AnimalSales), medianWithoutNA(AnimalSales), AnimalSales),
         AnimalCV = ifelse(is.na(AnimalCV), medianWithoutNA(AnimalCV), AnimalCV),
        FruitNutSales  = ifelse(is.na(FruitNutSales ), medianWithoutNA(FruitNutSales ),      FruitNutSales ),
         FruitNutCV  = ifelse(is.na(FruitNutCV), medianWithoutNA(FruitNutCV), FruitNutCV),
         VeggieSales  = ifelse(is.na(VeggieSales), medianWithoutNA(VeggieSales), VeggieSales),
         VeggieCV  = ifelse(is.na(VeggieCV), medianWithoutNA(VeggieCV), VeggieCV),
         CropSales = ifelse(is.na(CropSales), medianWithoutNA(CropSales), CropSales),
         CropCV  = ifelse(is.na(CropCV ), medianWithoutNA(CropCV ), CropCV ),
          AgLandAcres = ifelse(is.na(AgLandAcres), medianWithoutNA(AgLandAcres), AgLandAcres),
          AgLandCV = ifelse(is.na(AgLandCV), medianWithoutNA(AgLandCV), AgLandCV)
   ) %>% ungroup()
```


```{r}
tot_na3 = apply(U3, 2, function(x) length(which(is.na(x))))
tot_na3
```

Now we write the csv file to be used again later.  It should be noted that this data set uses the current set of FIPS codes, which changed in 2015.  There were a few changes to the codes, particularly in the state of Virginia.
```{r}
USDA_data_cleaned = U3
write.csv(U3, "Cleaned Data/USDA_data.csv")
```


## Election Data
This data set contains many useful pieces of information compiled through several sources. (https://github.com/Deleetdk/USA.county.data).  


```{r}
dim(Election2016)
str(Election2016[ ,1:25])
length(unique(Election2016$County))
```

Some information is redundant.  We will keep the percentages of votes by political party, and exclude the columns that contain the number of votes per party.  

```{r}
E1 = Election2016 %>%  filter(ST != "AK") %>% dplyr::select(State:Votes, Republicans.2016:Autumn.Tmin, temp, precip) 
```

Checking NA's.
```{r}
tot_na1 = apply(E1, 2, function(x) length(which(is.na(x))))
tot_na1[tot_na1 > 0]
```
Homicide rates and infant mortality have too many NA's so it will be dropped.  Otherwise state medians will be used to impute the remaining missing values.  There are many missing temperatures but these will be imputed by state medians, which should be fairly representative of the county.
```{r}
E2 = E1 %>% dplyr::select(-Homicide.rate, -Infant.mortality ) %>%
  group_by(State) %>%
  mutate_each(funs(ifelse(is.na(.),median(., na.rm = TRUE),.)))  %>% ungroup()
```
Checking NA's.
```{r}
tot_na2 = apply(E2, 2, function(x) length(which(is.na(x))))
tot_na2[tot_na2 > 0]
```
Remaining missing will be imputed by global column medians.
```{r}
E3 = E2  %>%
  mutate_each(funs(ifelse(is.na(.),median(., na.rm = TRUE),.))) 
```
Checking NA's.
```{r}
tot_na3 = apply(E3, 2, function(x) length(which(is.na(x))))
tot_na3[tot_na3 > 0]
```
Good to go!
```{r}
Election2016Cleaned = E3
write.csv(E3, "Cleaned Data/Election2016Cleaned.csv")
```

## Census Data
Use `tidycensus` to link up with census API. 

First load avaiable variables 
```{r}
census_variables = load_variables(year = 2010, dataset = "sf1", cache = TRUE)
```
I would like the following variables:
              family to household ratio  = P018002/P018001
              married household to family household ratio = P018003/P018002
              urban ratio = H002002 / H002001
              renter occupied = 		H004004 / H004001
              household size categories = H013002:H013008 / H013001
              Ave househould size = 	H012001
              Ave family size  = P037A001
              total female pop = P012026/ P012001
              male age groups =   (P012003:7 (under 20)), (P012008:10 (20-24)) , 
                                   (P012011:14( 25-44)), (P012015:6 (45-54)), 
                                   (P012017 (55-59)), (P012018:25 (60+))
                                  / P012002 (total)
              female age groups =   (P0120273:31 (under 20)), (P012032:34 (20-24)) , 
                                   (P012038:38( 25-44)), (P012039:40 (45-54)), 
                                   (P012041 (55-59)), (P012042:49 (60+))
                                  / P012026 (total)
              husband - wife - children families ratio = 	P038003/ 	P038001
```{r}
vars = c(paste0("P01800",1:3), "H002001", "H002002", "H004001" , "H004004", "H012001",
         paste0("H01300", 1:8), "P037A001", paste0("P01200", 1:9), paste0("P0120", 10:49),
         "P038001", "P038003")
```


```{r}
states = c(state.name, "District Of Columbia") ## get the state names
states =states[-2] ## take out Alaska
census_raw_data = map_dfr(
  states,
  ~ get_decennial(
    geography = "county",
    variables = 	vars,
    state = .,
    year = 2010,
    geometry = FALSE, 
    output = "wide"
  )
) 

```

Now let's turn this raw data into the ratios I'm after and drop the raw numbers.  These are the first of my derived variables.

```{r}
Census_data_cleaned = census_raw_data %>% rename(Fips = GEOID) %>% 
                          mutate(FamilyRatio = P018002/P018001,
                                 MarriedHouseholdRatio = P018003/P018002,
                                 RenterOccupied = H004004/H004001,
                                 AveHousehouldSize = 	H012001,
                                 AveFamilySize  = P037A001,
                                 TotalFemale  = P012026/ P012001,
                                 MaleUnder20 =  add_cols(.,  P012003:P012007)  / P012002,
                                 Male20to24 = add_cols(.,P012008:P012010) / P012002,
                                 Male25to44 = add_cols(.,P012011:P012014) / P012002,
                                 Male44to54 = add_cols(.,P012015:P012016) / P012002,
                                 Male55to59 = P012017 / P012002,
                                 MaleOver59 = add_cols(.,P012018:P012025) / P012026,
                                 FemaleUnder20 = add_cols(.,P012027:P012031) / P012026,
                                 Female20to24 = add_cols(.,P012032:P012034) / P012026,
                                 Female25to44 = add_cols(.,P012035:P012038) / P012026,
                                 Female44to54 = add_cols(.,P012039:P012040) / P012026,
                                 Female55to59 = P012041 / P012026,
                                 FemaleOver59 = add_cols(.,P012042:P012049) / P012026,
                                 HusbandWifeFamilyRatio = P038003/ 	P038001
    
                                 ) %>% dplyr::select(-all_of(vars))
```
Just like a mirror -looking good!



def's for the census data
https://www.census.gov/programs-surveys/cps/technical-documentation/subject-definitions.html#household

## Zip Code Referential Table
The farmers market table contains some incomplete records.  The county of each farmers market is needed but missing or unmatched on the farmers market table.  Additionally, there is no FIPS code provided on the table. The purpose of this section have a reference so a zip code can be matched to its county.  However, some zip code span more than one county.  To resolve this, it has been decided to match each zip code with the county where the most amount of residents from that zip code.

A copy of a zip code to county subdivision reference table is available through the US Census Bureau.  
```{r}
str(zips_in_county_subdiv)
```
This table contains data on the population, number of housing units, land area for section of the zip code contained in each county.  The "CS" prefix refers to the county sibdivision of that zip code.  A county is composed of multiple subdivisions, and we can use that fact to calculate the total land area and housing units of each county given this table.
```{r}
Z1 = zips_in_county_subdiv %>% group_by(STATE, COUNTY) %>% 
  mutate(CountyHU = sum(CSHU), CountyArea = sum(CSAREA)) %>% ungroup()
```

The zip sections are arranged by descending population.  FIPS codes are added and only the most popluous section of each unique zip code is selected.
```{r}
Z2 = Z1 %>% arrange(ZCTA5, desc(POPPT)) %>% mutate(Fips = paste0(STATE, COUNTY))  %>% select(-CLASSFP)##sort by zip and population
zips = unique(zips_in_county_subdiv$ZCTA5) ## keep only the first unique zip
Z3 = Z2[!duplicated(Z2$ZCTA5),  ] #clean
```
The `tidyCENSUS` package contians the county names corresponding to each FIPS code.  We create a reference table.
```{r}
County_Code_Ref = fips_codes %>% mutate(Fips = paste0(state_code, county_code))
head(County_Code_Ref)
```
Lastly we join the Z3 table with the County_Code_Ref table to include county names.
```{r}
Z4 = left_join(Z3, County_Code_Ref %>% dplyr::select(Fips, county, state_name, state), by = "Fips") %>% rename(ST = state, Zip = ZCTA5)
```
Lastly rearrange to be more user-friendly.
```{r}
zip_unique = Z4 %>% dplyr::select(Zip, Fips, county, state_name, ST, STATE:CountyArea)
write.csv(zip_unique, "Cleaned Data/Zip Unique.csv")
```

## Farmers Market Data Set Cleaning
A frequency of the number of farmers market per county will be created.  Missing or incomplete county names will need to be imputed by zip code.  If a zip code is missing, the `zipcode` package can match cities to zip codes, although some cities span more than one zip code.

```{r}
dim(farmers_markets)
str(farmers_markets[ ,1:30])
```
 Inspecting the zip codes.
```{r}
table(nchar(farmers_markets$zip))
sum(is.na(farmers_markets$zip))
```
Some zip code are missing leading 0's, some have a hyphenated suffix, some have mistakes, and some are missing.  We add an ID column and add leading 0's to 3 and 4 digit zips.  Additionally, we need the county names to match up correctly in order to locate the FIPS code.  So we trim whitespace, fix capitalization, and add Parish to county names in Louisiana.
```{r}
farmers_markets = cbind( ID = 1:nrow(farmers_markets), farmers_markets)
F1 = farmers_markets %>% mutate(City = str_to_title(trimws(city, which = "both")),
                                State = str_to_title(trimws(State)),
                                County = str_to_title(County),
                                County = str_replace(County, "County",""),
                                County = trimws(County, which = "both"),
                                ST = state2abbr(State),
                                zip = ifelse(nchar(zip) == 3, paste0("0",zip), zip),
                                zip = ifelse(nchar(zip) == 4, paste0("0",zip), zip),
                                zip = ifelse(nchar(zip) == 10, str_sub(zip, 1, 5), zip),
                                County = ifelse(State == "LA", paste0(County, "Parish"),
                                                paste(County, "County", sep =" "))) %>%
                          select(ID, City, County, State, ST, zip)
```
Next we use the `zipcode` package to find zip codes using city and state names if a remainig zip code does not have five digits.
```{r}
data("zipcode")
F2 = F1 %>% mutate(zip = ifelse(nchar(zip == 5), zip,
                               zipcode$zip[zipcode$city == City & zipcode$state == ST] ),
                   County = ifelse(ST == "DC", str_replace(County, "County", ""), County))
sum(is.na(F2$County))
```
There are stll a few missing counties, which we will deal with later.
```{r}
F3 = left_join(F2, County_Code_Ref, by = c(c("County"="county"), c("ST" = "state")), all.x=T) %>%
        mutate(Fips = ifelse(ST == "DC", "11001", Fips)) %>%
        select(-state_name)
```
Now we drop our first observations outside the lower 49 states and find out how many FIPS codes are still missing. 
```{r}
dropped_IDs = F3$ID[F3$State %in% c("Alaska", "Puerto Rico", "Virgin Islands")] ## create running vector of dropped ID's
F4 = F3 %>% filter(!State %in% c("Alaska", "Puerto Rico", "Virgin Islands"))
sum(is.na(F4$Fips))
```
We did drop 83 farmers markets, but have 326 FIPS codes to find.  We put these observations into a new data frame.  Let's see if there zip codes seem reliable.
```{r}
na_county = F4[is.na(F4$Fips), c(1,2,3,6)]
table(nchar(na_county$zip))
```
Most of these missing do have zip codes so let's see if we can find the counties using the zip code reference table.
```{r}
found_county = left_join(na_county, zip_unique, by = c("zip" = "Zip" ) ) %>% select(ID:ST)
```
These found counties will be joined to the running farmers market table. 
```{r}
F5 = left_join(F4, found_county, by = "ID")
F6 = F5 %>% mutate(County = ifelse(is.na(County.x), County.y, County.x),
                   Fips = ifelse(is.na(Fips.x), Fips.y, Fips.x)) %>%
            select(ID, City.x, County, State, ST.x, zip.x, state_code, county_code, Fips )
colnames(F6) = gsub(".x", "", colnames(F6))
sum(is.na(F6$Fips))
```
Now we're left with 52 Fips to find.

As many of the last few remaining NA's are located.
```{r}
na_county2 = F6[is.na(F6$Fips), ] %>% 
                mutate(County = str_replace(County, "County", ""),
                       County = trimws(County, which = "both"),
                       County = ifelse(ST == "LA", paste(County, "Parish", sep = " "),
                                       paste(County, "County", sep =" ")),
                       County = str_replace(County, "St.", "Saint"),
                       County = ifelse(str_sub(County, 1, 2) %in% c("De", "Mc", "O'", "Du"),
                                       paste0(str_sub(County, 1,2), str_to_upper(str_sub(County, 3,3)), str_sub(County, 4,-1)),  County),
                       County = ifelse(City == "Colorado Springs", "El Paso County", County),
                       County = ifelse(City == "St. Louis", "St. Louis County", County),
                       County = ifelse(County == "Fond Du Lac County", "Fond du Lac County", County)
                       )
found_county2 = left_join(na_county2, County_Code_Ref, by = c(c("County" = "county"), c("ST" = "state")))                       
```

Join these last counties that were able to be matched to the running farmers market table, and drop the remaining.
```{r}
dropped_IDs = c(dropped_IDs,found_county2$ID[is.na(found_county2$Fips.y)])

found_county2 = found_county2[!is.na(found_county2$Fips.y),] %>%
                    select(ID, City, County, State, ST, zip, 
                           Fips.y, state_code.y, county_code.y)
## merge with running farmers market table
F7 = left_join(F6, found_county2, by = "ID") %>%
        mutate( Fips = ifelse(is.na(Fips), Fips.y, Fips),
                County = ifelse(is.na(Fips), County.y, County.x)) %>%
        select(ID, City.x, County.x, State.x, ST.x, zip.x, state_code, 
               county_code, Fips) 
colnames(F7) = gsub(".x","", colnames((F7)))
```

We create a CSV file containing each farmers market's FIPS code.
```{r}
length(dropped_IDs) ## only had to drop 116 of 8,804! (some are mobile markets)
FarmersMarketEach = F7
write.csv(F7, "Cleaned Data/FarmersMarketEach.csv")  ## each market
Droppped_FM = farmers_markets[dropped_IDs, ] ##data frame of dropped markets
```

Of our 8,804 original farmers markets, we dropped 116 which were either outside the lower 49 states or we were unable to match the market to one county. It should be noted that some of these markets are "mobile markets."

Next we write a csv file with each market.  This is not the frequency table we're after, but this is a useful table for later applications.
```{r}
## lastly make a freq table by each Fips. Only lower 49 states
Fips_table = data.frame(table(F7$Fips)) %>% rename( Fips = Var1) %>% mutate_if(is.factor, as.character) 
F8 = left_join(County_Code_Ref %>% filter(!state %in% c("AK","PR","VI")), 
               Fips_table, by = "Fips", all.x = TRUE) %>% 
          mutate(Freq = ifelse(is.na(Freq), 0, Freq))
AllCountyFMfreq = F8
write.csv(F8, "Cleaned Data/AllCountyFarmersMarketFreq.csv")
```

## Final Join
The last step now is to join the Election, USDA, Zip Code, and Farmers Market frequency tables together.

Below we join the election and USDA data to the farmer market frequency table, and inspect any NA's.
```{r}
V1 = full_join(AllCountyFMfreq %>% dplyr::select(Fips,county, state, state_name, Freq), USDA_data_cleaned, by = "Fips") 
V12 = full_join(V1, Election2016Cleaned, by = "Fips") %>% dplyr::select(-State.x, -County.x, -State.ANSI, -County.ANSI)
V2 = full_join(V12, Census_data_cleaned, by ="Fips")

tot_naF = apply(V2, 2, function(x) length(which(is.na(x))))
tot_naF[tot_naF > 0 ]
```
Here is a view of our last few NA's.
```{r}
final_nas1 = V2[is.na(V2$Ag.District), 1:16]
final_nas2 = V2[is.na(V2$MAR), 1:16]
final_nas3 = rbind(final_nas1,final_nas2)
final_nas4 = final_nas3[!duplicated(final_nas3$Fips), ]
final_nas4
```

The 12 rows missing election data and census data, which all have 0 farmers market frequency, are dropped.
```{r}
## drop rows with both missing Election2016 data from running final table
V3 = V2 %>% filter( !is.na(MAR))  ## all row is missing and no Freq's lost
dropped_fips = setdiff(V2$Fips, V3$Fips)
```

The county area and housing unit from the zips table needs to be added.  

```{r}
V4 = left_join(V3, zip_unique %>% dplyr::select(Fips, CountyArea, CountyHU), by = "Fips") %>% dplyr::distinct() %>% dplyr::select( -state, -County.y, -State.y)
```

Last use state medians to impute missing USDA data.  The state of Virginia has multiple entries imputed.
```{r}
V5 = V4 %>% group_by(state_name) %>%
  mutate(CountyArea = ifelse(is.na(CountyArea), medianWithoutNA(CountyArea), CountyArea),
         CountyHU = ifelse(is.na(CountyHU), medianWithoutNA(CountyHU), CountyHU),
         AnimalSales = ifelse(is.na(AnimalSales), medianWithoutNA(AnimalSales), AnimalSales),
         AnimalCV = ifelse(is.na(AnimalCV), medianWithoutNA(AnimalCV), AnimalCV),
         FruitNutSales  = ifelse(is.na(FruitNutSales ), medianWithoutNA(FruitNutSales ), FruitNutSales ),
         FruitNutCV  = ifelse(is.na(FruitNutCV), medianWithoutNA(FruitNutCV), FruitNutCV),
         VeggieSales  = ifelse(is.na(VeggieSales), medianWithoutNA(VeggieSales), VeggieSales),
         VeggieCV  = ifelse(is.na(VeggieCV), medianWithoutNA(VeggieCV), VeggieCV),
         CropSales = ifelse(is.na(CropSales), medianWithoutNA(CropSales), CropSales),
         CropCV  = ifelse(is.na(CropCV ), medianWithoutNA(CropCV ), CropCV ),
         AgLandAcres = ifelse(is.na(AgLandAcres), medianWithoutNA(AgLandAcres), AgLandAcres),
        AgLandCV = ifelse(is.na(AgLandCV), medianWithoutNA(AgLandCV), AgLandCV)

  ) %>% ungroup() 
```


Let's see what is still missing.
```{r}
tot_na = apply(V5, 2, function(x) length(which(is.na(x))))
tot_na[tot_na > 0 ] ## remainig NA's
V5[is.na(V5$AnimalSales),1:10]  ## the missing USDA fips
```
Since agriculture is likely limited in DC, it will be assigned the minimum value for each missing agricultural variable.
```{r}
V5[291,6:15] = as.list(apply(V5[ ,6:15],2, function(x) min(x, na.rm = TRUE)))
```

Last remaining NA's.
```{r}
V5[is.na(V5$Ag.District),1:8 ]
```

Many Virginia cities are missing the agricultural district name, so those missing will get their own category.  Otherwise the rest will be in an "unknown" category.
```{r}
V5[is.na(V5$Ag.District) & V5$ST == "VA", 5] = "Virginia (Unknown)"
V5[is.na(V5$Ag.District), 5] = "Unknown"
V5 = data.frame(V5)
```

Let's check to see if all NA's are gone and the frequencies add up correctly.
```{r}
tot_na5 = apply(V5, 2, function(x) length(which(is.na(x))))
tot_na5[tot_na5 > 0 ]
sum(V5$Freq) ## adds up correctly! 8804 original with 116 dropped
8804 - 116
```
We add row names for presentation by concatenating county nd state names.  Then we rename and reorder the columns.
```{r}
rn = make.names( paste(V5$county, V5$ST, sep = " "), unique = TRUE)
V55 = V5 %>% mutate(RowName = rn) %>% rename(County = county, State = state_name)

V6 = V55 %>% dplyr::select(Fips, RowName, County, State, Ag.District, Freq, CountyArea, CountyHU, AnimalSales:CountyHU)
```

Let's make some derived variable looking at the change in votes from 2008 and 2012 for both politcal parties.  Also inlude the range from min amd max temeratures as a measure of temperature variability.   Additionally, some of the race and weather variables are redundant, so they will be dropped.

```{r}
V66 = V6 %>% mutate(
                 DemChange08 = 100*(Democrats.2016 - Democrats.2008)/Democrats.2008,
                 DemChange12 = 100*(Democrats.2016 - Democrats.2012)/Democrats.2012,
                 RepChange08 = 100*(Republicans.2016 - Republicans.2008)/Republicans.2012,
                 RepChange12 = 100*(Republicans.2016 - Republicans.2012)/Republicans.2008,
                 OtherRace = Other,
                 TempRange = Annual.Tmax - Annual.Tmin) %>% dplyr::select(-Asian.American.Population,  
                 -Native.American.Population, -Annual.Prcp, -White..Not.Latino..Population, -Latino.Population,
                 -African.American.Population, -Other.Race.or.Races, -Other  )
```



And we are done!
```{r}
All_Final_Data_Cleaned = V66 %>% dplyr::select(-ST, -NAME)
rownames(All_Final_Data_Cleaned) = All_Final_Data_Cleaned$RowName
write.csv(All_Final_Data_Cleaned, "Cleaned Data/All_Final_Data_Cleaned.csv")
head(All_Final_Data_Cleaned)
```
Our final data set contains 3,114 rows (counties) of 126 variables.  

Derived Variables:  
DemChange08, DemChange12, RepChange08, RepChange12, TempRange, FamilyRatio, MarriedHouseholdRatio,   RenterOccupied, AveHousehouldSize, AveFamilySize, TotalFemale, MaleUnder20, Male20to24, Male25to44, Male44to54, Male55to59, MaleOver59, FemaleUnder20, Female20to24, Female25to44, Female44to54, Female55to59, FemaleOver59, HusbandWifeFamilyRatio 

Droppped rows IDs from original farmers markets data set.
```{r}
dropped_IDs
```

## Data Visualization and Exploration

Let's start by making some aliases to facilitate the coding.
```{r}
## alias for master tables
D1 = All_Final_Data_Cleaned
D2 = farmers_markets
D3 = FarmersMarketEach
```

Use `tidycensus` to upload county geography using the API (requires internet connection and may take a few minutes).  I also had geo.shapes file on the election metadata.  I think there is a difference in FIPS codes since the 2015 switch between the two files, so I'll probably end up using the `geo` variable.  

Here is some basic info.
```{r}
## Pie chart
B1 = data.frame(table(D1$Freq)) %>% rename( Markets = Var1) ## freq tables of # of markets
B2 = B1 %>% mutate(Y = ifelse(B1$Markets != "0" ,1,0)) ## make an "at least one" category
X3 = c("Zero", "At least One") ## name it
Y3 =c()
Y3[1] = sum(B2$Freq[B2$Y == 0]) ## total 0's
Y3[2] = sum(B2$Freq[B2$Y == 1]) ## total "at least one"
B3 = data.frame(X3, Y3 ) %>% rename( Freq = Y3)

pie <- ggplot(B3, aes(x="", y=Freq, fill=X3))+
  geom_bar(width = 1, stat = "identity") + coord_polar("y")
PieChart = pie + theme_minimal() + ggtitle( "US Farmers Markets Per County") +
  theme(plot.title = element_text( face = "bold")) + 
  ylab("n = 3,114 counties") + xlab("") +
  labs(fill = "") + geom_text(aes(y = Freq/2 + c(0, cumsum(Freq)[-length(Freq)]),
                                  label = paste(round((Freq/sum(B3[ ,2]))*100,2), "%",sep="")), size=3) + 
  scale_fill_manual(values = c("khaki2", "olivedrab4"))
  
PieChart
```
Nearly 3/4 of counties have at least one farmers market.

```{r}
ggplot(All_Final_Data_Cleaned, aes(x=Freq, )) + geom_histogram( fill = "olivedrab4", binwidth = 1) + labs( x= "Number of Farmers Market per County", y ="Frequency", title = "Distribution of Response Variable") + theme(plot.title = element_text(face = "bold", size = 14))
```
The distribution of the number of farmers markets per county is skewed right.  Most counties have 0-2 farmers markets.  There are some outliers, like Los Angeles County with 128 farmers markets (and about 10 million residents).  

Since the response variable is a count variable, it is likely to follow either a Poisson or negative binomial distribution, if there is no overdispersion present.  A zero inflated model should be investigated, as there may be factors present in some counties that prevent the county from having any farmers markets.

Next let's see the distribution of markets by state.
```{r}
B3 = data.frame(table(FarmersMarketEach$State)) %>% rename( State = Var1) %>% 
        arrange(Freq) %>% filter(State != "Virgin Islands")
BarchartStates = B3  %>%
                      ggplot(aes(x = reorder(State, Freq), y = Freq)) + 
                      geom_bar(stat="identity", width=0.5, fill =  "olivedrab4", 
                               color = "gold")  + coord_flip() +
                      ylab("Number of Farmers Markets by State") + xlab("") +
                      theme(axis.text.y = element_text(color = "grey20",   
                                  size = 6.5, angle = 0, face = "plain"),
                            axis.title.x = element_text(size = 14, face = "bold"), 
                            axis.title.y = element_text(size = 14, face = "bold")) +
                      geom_text(aes(label=Freq), hjust=-.3, color="blue",
                             position = position_dodge(0.9), size=2.6) +
                      scale_y_continuous(limits = c(0, 800))
  
BarchartStates      
```


 
It is natural to inspect the relationship on the number of farmers markets between county population and median income.
```{r}
ggplot(All_Final_Data_Cleaned, aes(x = Total.Population, y = Median.Earnings.2010 )) + geom_point( aes(size = Freq, color = Freq)) + guides(size=FALSE) + labs(title = "Population and Income on County Farmers Markets")  + geom_label_repel(aes(label=ifelse(Freq>45 ,as.character(RowName),'')),hjust=2,vjust=3, size = 3 ) + scale_x_continuous(labels = comma) + scale_color_distiller( palette = "YlOrBr", direction = 1, name = "Farmers \nMarkets \nFreq.") 
```
There is a moderate positive relatioship between the number of farmers markets (size and color of bubbles) and county population.  There is also a weak positive relationship between the numer of farmers markets and county 2010 median income.


```{r}
ggplot(All_Final_Data_Cleaned, aes(x = AveFamilySize, y = Freq )) + geom_point() + labs(title = "Average Family Size and County Farmers Markets")  + geom_label_repel(aes(label=ifelse(Freq>45 ,as.character(RowName),'')),hjust=2,vjust=3, size = 3 ) + scale_x_continuous(labels = comma) 
```

# Map Data

Let's upload shape files from the Census Bureau.
```{r}
states = c(state.name, "District Of Columbia") ## get the state names
states =states[-2] ## take out Alaska
## Below is API to US Census Bureau to get county geography
Geography = map_dfr(
  states,
  ~ get_acs(
    geography = "county",
    variables = 	"B00001_001",
    state = .,
    year = 2018,
    geometry = T, 
    output = "wide"
  )
) 
```

```{r}
G1 = left_join(Geography %>% dplyr::select(GEOID, geometry), All_Final_Data_Cleaned, by = c( "GEOID" = "Fips" )) %>% filter(State != "Hawaii")  %>% dplyr::select( -GEOID) ## add geometry shape to final data
rownames(G1) = G1$RowName 
```



```{r}
brks = c(0,1,2,4,8,16,32,128)
tm_view()
tm_shape(G1) + tm_fill("Freq", breaks = brks, palette = "YlGn") + 
  tm_layout(legend.position = c(.87,.25),
            legend.text.size = .5, 
            title.position = c("center","top"),
            title = "Number of Farmers Markets by County",
            title.fontface = "bold" ,
            title.size = 1)
```


```{r}
brks2 = c(0,2500,5000,10000,20000,50000,100000, 200000, 500000, 1000000, 3000000, 12000000)
tm_view()
tm_shape(G1) + tm_fill("Total.Population", breaks = brks2, palette = "BuGn") + 
  tm_layout(legend.position = c(.87,.25),
            legend.text.size = .5, 
            title.position = c("right","bottom"),
            title = "Population by County",
            title.fontface = "bold" ,
            title.size = 1) 
```

Add "markets per 1,000 residents' variable.

```{r}
G1$MPT = 1000* G1$Freq / G1$Total.Population
```



```{r}
tm_view()
tm_shape(G1) + tm_fill("MPT", palette = "YlOrBr") + 
  tm_layout(legend.position = c(.87,.25),
            legend.text.size = .5, 
            title.position = c("center","top"),
            title = "Farmers Markets Per 1,000 Residents",
            title.fontface = "bold" ,
            title.size = 1)
```

## PCA and Hierarchial Clustering
PCA is used to investigate the relationship between the individuals and variables.


Create a new data frame for PCA use.  PCA will use Ag.District and State as supplemental qualitative variables, and Freq as a supplemental quantitiative variable.  All other quantitative variables will be used to construct the dimensions.
```{r}
D1 = All_Final_Data_Cleaned
rownames(D1) = All_Final_Data_Cleaned$RowName
D2 =  apply(D1[ ,c( 4:5)], 2, as.factor) %>% data.frame()
D3  = apply(D1[ ,c(6:ncol(D1))], 2, as.numeric) %>% data.frame()
D4 = cbind(D2, D3)
D4$State = as.factor(D4$State) # complete unstandarized data set
D4$Ag.District = as.factor(D4$Ag.District) # complete unstandarized data set
str(D4[ ,1:10])
apply(D4, 2, function(x) sum(is.na(x)))[apply(D4, 2, function(x) sum(is.na(x))) > 0] ##how many NA's > 0
```
For the PCA, we assume a linear reationship between the variables.  Variables are normalized by `scale.unit  = TRUE` option.  Frequency is a supplemental quanititative variable that is not used in the construction of the dimensions.

```{r}
fm.pca = PCA(D4, scale.unit  = TRUE, ncp = 30, quanti.sup = 3, quali.sup = 1:2, graph = TRUE)
```

Here is the scree plot.
```{r}
barplot(fm.pca$eig[,2][1:30], main = "scree plot",  
        ylab = "percent of total variance", 
        xlab = "first 30 principle inertia", col = "skyblue")

```
There are noticible drops in the variation between dimensions 5 and 6 and also dimensions 13 and 14.

```{r}
plot(1:30, fm.pca$eig[1:30 ,3], pch =20, xlab = "Dim. #", ylab = "Cumulative % of Variance")
lines(1:30, fm.pca$eig[1:30 ,3])
abline( h = 80, lty =2, col = "skyblue")     
```


```{r}
p1 = fviz_pca_biplot(fm.pca, axes = 1:2, select.ind =  list(contrib = 50),
                select.var = list(contrib = 30), 
             gradient.cols = "lightgreen", col.ind ="forestgreen",
             repel = TRUE, labelsize = 2, col.var = "orange3" , col.ind.sup = "skyblue", 
             fill.ind = "olivedrab4", 
             col.quanti.sup = "red" , fill.var = "orange3", pointsize = .7 ) + theme_minimal() +
             ggplot2::annotate("text", x = 12, y = 1, label = "Hotter", col = "red") +
             ggplot2::annotate("text", x = -12, y = 1, label = "Colder", col ="blue") +
             ggplot2::annotate("text", x = 0, y = 25, label = "Politcally Left", col = "blue") +
             ggplot2::annotate("text", x = 0, y = -15, label = "Politcally Right", col ="red")
fviz_add(p1, data.frame(fm.pca$quali.sup$coord[1:50, ], fm.pca$quali.sup$coord[1:50, ]), repel = TRUE, labelsize = 2.3, pointsize = .7 , color = "deepskyblue3") 
# Contributions of variables to PC1
fviz_contrib(fm.pca, choice = "var", axes = 1, top = 30)
# Contributions of variables to PC2
fviz_contrib(fm.pca, choice = "var", axes = 2, top = 30)
```
The first dimension consists primarily of temperature data - warmer climates are on the positive side of the x-axis while colder climates are on the negative x-axis.  

The second dimesnion seperates politcally left leaning counties on the positive side of the y-axis, with politically right leaning counties on the negative y-axis.  Most of the "blue states" are above the x-axis, while "red states" are below the x-axis.

Variables, states, and/or counties near each other on the biplot between the first and second dimensions can be interprested as similar with respect to temperature and politcal leaning.  For example, Los Angeles County, located near the top of the plot, has a large y-value, implying it is very left leaning politcally.  It's moderate positiion on the x-axis indicates the temperatures are moderately above average.  


```{r}
p2 = fviz_pca_biplot(fm.pca, axes = 3:4, select.ind =  list(contrib = 50),
                select.var = list(contrib = 30), 
             gradient.cols = "lightgreen", col.ind ="forestgreen",
             repel = TRUE, labelsize = 2, col.var = "orange3" , col.ind.sup = "skyblue", 
             fill.ind = "olivedrab4", 
             col.quanti.sup = "red" , fill.var = "orange3", pointsize = .7 ) + theme_minimal() +
             ggplot2::annotate("text", x = 9, y = 1, label = "Large Families", col = "red") +
             ggplot2::annotate("text", x = -9, y = 1, label = "Small Families", col ="blue") +
             ggplot2::annotate("text", x = 0, y = 10, label = "Wetter", col = "blue") +
             ggplot2::annotate("text", x = 0, y = -20, label = "Drier", col ="red")
fviz_add(p2, data.frame(fm.pca$quali.sup$coord[1:50, ], fm.pca$quali.sup$coord[1:50, ]), repel = TRUE, labelsize = 2.3, pointsize = .7 , color = "deepskyblue3") 
# Contributions of variables to PC3
fviz_contrib(fm.pca, choice = "var", axes = 3, top = 30)
# Contributions of variables to PC4
fviz_contrib(fm.pca, choice = "var", axes = 4, top = 30)
```
The thrid dimension largely seperates family size.  The fourth dimesnsion seperates precipation. 


```{r}
p3 = fviz_pca_biplot(fm.pca, axes = 5:6, select.ind =  list(contrib = 50),
                select.var = list(contrib = 30), 
             gradient.cols = "lightgreen", col.ind ="forestgreen",
             repel = TRUE, labelsize = 2, col.var = "orange3" , col.ind.sup = "skyblue", 
             fill.ind = "olivedrab4", 
             col.quanti.sup = "red" , fill.var = "orange3", pointsize = .7 ) + theme_minimal() +
             ggplot2::annotate("text", x = 15, y = 1, label = "Large Families", col = "red") +
             ggplot2::annotate("text", x = -15, y = 1, label = "Small Families", col ="blue") +
             ggplot2::annotate("text", x = 0, y = 20, label = "More Diverse", col = "blue") +
             ggplot2::annotate("text", x = 0, y = -15, label = "Less Diverse", col ="red")
fviz_add(p3, data.frame(fm.pca$quali.sup$coord[1:50, ], fm.pca$quali.sup$coord[1:50, ]), repel = TRUE, labelsize = 2.3, pointsize = .7 , color = "deepskyblue3") 
# Contributions of variables to PC5
fviz_contrib(fm.pca, choice = "var", axes = 5, top = 30)
# Contributions of variables to PC6
fviz_contrib(fm.pca, choice = "var", axes = 6, top = 30)
```


```{r}
All_Data_PCA = data.frame(cbind(fm.pca$ind$coord ,All_Final_Data_Cleaned$Freq)) %>% dplyr::select(V31, Dim.1:Dim.30) %>% rename(Freq = V31) 
write.csv(All_Data_PCA, "All_Data_PCA.csv")
```



## CLUSTERING
Goal- identify variables that best seperate county profiles

Choose 7 clusters (Drop in inertia gain).  Choose best variables to represent each cluster.
```{r}
fm.hcpc<-HCPC(fm.pca ,nb.clust=7 ,graph=TRUE, description = TRUE) 
```


```{r}
print("########## CLUSTER 1 ##########")
round(fm.hcpc$desc.var$quanti[[1]][ ,c(1,2,3,6)],3)
print(c("########## CLUSTER 2 ##########"))
round(fm.hcpc$desc.var$quanti[[2]][ ,c(1,2,3,6)],3)
print(c("########## CLUSTER 3 ##########"))
round(fm.hcpc$desc.var$quanti[[3]][ ,c(1,2,3,6)],3)
print(c("########## CLUSTER 4 ##########"))
round(fm.hcpc$desc.var$quanti[[4]][ ,c(1,2,3,6)],3)
print(c("######### CLUSTER 5 ##########"))
round(fm.hcpc$desc.var$quanti[[5]][ ,c(1,2,3,6)],3)
print(c("########## CLUSTER 6 ##########"))
round(fm.hcpc$desc.var$quanti[[6]][ ,c(1,2,3,6)],3)
print(c("########## CLUSTER 7 ##########"))
round(fm.hcpc$desc.var$quanti[[7]][ ,c(1,2,3,6)],3)
```

CLUSTER 1: Older male population, higher elevation, farming, conservative, large area, small houshold size, married (retired??)

CLUSTER 2:  educated, married, more Trump over Obama, females 44-59, insured, healthy

CLUSTER 3:  high income, educated, liberal, married, high ave. household size, employed, healthy

CLUSTER 4: hot, poor health, construction and transportation jobs, single parents, low income, republican

CLUSTER 5: Hispanic, young, farming counties, lower education levels, 

CLUSTER 6: young, renters, educated, democrats,  multi-cultural, 

CLUSTER 7: democrats, poor health, low education, renters, low family size


The number of farmers markets per county is positively correlated with clusters 3 and 5, and negatively correlated with clusters 1, 2, 4, 5, and 7.

Assign cluster and PCA coordinates to final data set.

```{r}
All_Final_Data_Cleaned2 = cbind(All_Final_Data_Cleaned[ ,1:2], fm.hcpc$data.clust, fm.pca$ind$coord) %>% mutate_if(is.factor, as.character)
write.csv(All_Final_Data_Cleaned2, "Cleaned Data/All_Final_Data_Cleaned2.csv")
```

Oh lets map the clusters.

Let's add the geography to create a shape file.
```{r}
G2 = left_join(G1,  All_Final_Data_Cleaned2, by = "RowName")
```


```{r}
tm_view()
tm_shape(G2) + tm_fill("clust",  palette = "Set1") + 
  tm_layout(legend.position = c(.87,.25),
            legend.text.size = .5, 
            title.position = c("right","bottom"),
            title = "Cluster",
            title.fontface = "bold" ,
            title.size = 1) 
```

## Modeling and Analysis

```{r}
All_Final_Data_Cleaned2 %>% ggplot(aes(x = clust, y = Freq, color = clust)) + geom_point(aes(color = clust))  +
    scale_fill_brewer(palette="Set1") + labs(title="Farmers Markets by Cluster", face = "bold",x="Cluster", y = "Length") + theme_minimal()
```

## Modeling and Analysis

In this section, we will first explore whether certain variables, whose domains have been deemed signiificant in the past, have a significant effect on the frequency of farmers markets per county, while controling for certain variables.

Since the response variable is a count data, we will explore Poisson, quasipoisson, and negative binomials.  Zero inflated models will be investigated but the algorithms tend to fail to converge as the models became slightly more complex. Each variable or combination of variables will be tested in each of the aforementioned models, and the model with the lowest AIC will be selected.  AIC is preferred for this study since it is penalized by the addition of more parameters.  The selected model will be tested using a goodness of fit statistic.

A data frame of each model's predicted value, as well the chi-squared contribution, will be created to ultimately assess which counties deviate most from what is expected under the assumptions of te given model.

### Model 1: log(Total.Population)
We will use a log transformation on the popultion variable.  

```{r}
m1.pois = glm(Freq~ log(Total.Population), data = All_Final_Data_Cleaned2, family  = "poisson")
m1.nb = glm.nb(Freq~ log(Total.Population), data = All_Final_Data_Cleaned2)
m1.pois0 = zeroinfl(m1.pois, dist = "poisson")
m1.nb0 = zeroinfl(m1.pois, dist = "negbin")
L1 = list(m1.pois, m1.nb, m1.pois0, m1.nb0)
rbind(c("Poisson", "NegBin", "ZeroInflPoisson", "ZeroInflNegBin"),AIC = round(sapply( L1, AIC),2))
```

The negative binimial model is the preferred model.

```{r}
summary(m1.nb)
plot(m1.nb)
m1 = m1.nb
```


### Model 2: Family size 
Studies suggest that females 44-59 represent the largest demographic of shoppers at farmers markets, citing many are purchasing fresh produce o cook for their family.  Thus we lookat average family size, controlling for population.


```{r}
m2.pois = glm(Freq~ log(Total.Population) + AveFamilySize, data = All_Final_Data_Cleaned2, family  = "poisson")
m2.nb = glm.nb(Freq~ log(Total.Population) + AveFamilySize, data = All_Final_Data_Cleaned2)
m2.pois0 = zeroinfl(m2.pois, dist = "poisson")
m2.nb0 = zeroinfl(m2.pois, dist = "negbin")
L2 = list(m2.pois, m2.nb, m2.pois0, m2.nb0)
rbind(c("Poisson", "NegBin", "ZeroInflPoisson", "ZeroInflNegBin"),AIC = round(sapply( L2, AIC),2))
```

Again, the negative binimial model is the preferred model.

```{r}
summary(m2.nb)
plot(m2.nb)
m2 = m2.nb
```

## PCA
Now lets see how the coordinates of the PCA pan out.  We'll use the first 18 dimensions from the PCA, enough to account for 80% of variation between the dimensions.  We will start by using all dimensions to select a distribution. Then we will eliminate non-significant dimensions one at a time, removing the dimension with the highest p-value.


```{r}
m3.pois = glm(Freq~ ., data = All_Final_Data_Cleaned2 %>% dplyr::select(Freq, Dim.1:Dim.18), family  = "poisson")
m3.nb = glm.nb(Freq~ ., data = All_Final_Data_Cleaned2 %>% dplyr::select(Freq, Dim.1:Dim.18))
m3.pois0 = zeroinfl(m3.pois, dist = "poisson")
m3.nb0 = zeroinfl(m3.pois, dist = "negbin")
L3 = list(m3.pois, m3.nb, m3.pois0, m3.nb0)
rbind(c("Poisson", "NegBin", "ZeroInflPoisson", "ZeroInflNegBin"),AIC = round(sapply( L3, AIC),2))
```



Again, the negative binimial model is the preferred model.

```{r}
summary(m3.nb0)
m3 = m3.nb0
```

We can use backwards elimination to reduce dimesnsion.
```{r}
m4.nb0 = be.zeroinfl(m3.nb0, All_Final_Data_Cleaned2, dist = "negbin")
```

```{r}
summary(m4.nb0)
AIC(m4.nb0)
m4 = m4.nb0
```

## Analyst's Choice
The PCA analysis from before helps shed light on which variables are independent.  Furthermore, the previous research into customer profiles helps shed light on which variables give us reasonable information.  With this in mind, I set out to find some combination of variables that include measures of county population, political preference, family relations, age distribution, income, agricultural data, health, home ownership demographics, and eduacation.  Each variable or variables were selected to reduce AIC in the model while remaining as a significant predictor.  Extreme caution should be used in interpreting the results.

We can see the if the correlation between the predictors.
```{r}
M = All_Final_Data_Cleaned2 %>% dplyr::select(Freq, Total.Population,Green.2016  , Median.Earnings.2010 , FruitNutCV   ,Median.Age , RenterOccupied , Poor.physical.health.days ,Annual.Tavg , precip , Graduate.Degree)
corrplot(cor(M))
```
Although Graduate degree appears to be colinear with other factors, I havve chosen to let it remain in the model since it does tend to lower the AIC in the models I have experimented with and PCA has shown it to be a strong representation in the lower dimensons of the PCA.


```{r}
m5.pois = glm(Freq~  log(Total.Population)  + Green.2016  + Median.Earnings.2010 + FruitNutCV  + Median.Age + RenterOccupied + Poor.physical.health.days + Annual.Tavg + precip +  Graduate.Degree , data = All_Final_Data_Cleaned2, family  = "poisson")
m5.nb = glm.nb(Freq ~  log(Total.Population)  + Green.2016  + Median.Earnings.2010 + FruitNutCV  + Median.Age + RenterOccupied + Poor.physical.health.days + Annual.Tavg + precip +  Graduate.Degree , data = All_Final_Data_Cleaned2 )
L5 = list(m5.pois, m5.nb)
rbind(c("Poisson", "NegBin"),AIC = round(sapply( L5, AIC),2))
```

```{r}
summary(m5.nb)
plot(m5.nb)
m5 = m5.nb0
```

Goodness of Fit.
Here we test H0: The observed farmers market frequencies follow the given negative binomial model vs. H1: The observed farmers market frequencies do not follow the given negative binomial model using a chi-squared goodness of fit with N - p = 3114 - 11 = 3103 df. 
```{r}
qchisq(.95, 3103)
```
The 5% critical value is 3233.706, which is greater than the observed deviance of 2757.6.  So we fail to reject the null and can conclude that the given negative binomial model does fit the observed data.

Although it is possible to reduce AIC further, this model is can help us make some general interpretations about the frequency 


Lastly we can use zero inflated model with backwards selection with the remaining variables to reduce AIC to build a model with the highest predictive power.
```{r}
m5.nb0 = zeroinfl(m5.nb, data = All_Final_Data_Cleaned2, dist = "negbin")
m6 = be.zeroinfl(m5.nb0,data = All_Final_Data_Cleaned2, dist = "negbin")
AIC(m5.nb)
AIC(m6)
```

```{r}
summary(m6)
```


## Comparing Estimates
We'll make a data set containing the observed frequncy and the expected frequency under the last four models.  Models 1 and 2 are too simple and their AIC is too high for final consideration.  We construct the chi-squared component of each observation and model, and record whether the residual is possitive or negative.  


```{r}
Expected = data.frame(cbind(All_Final_Data_Cleaned2$Freq, m3$fitted.values, m4$fitted.values,
                 m5$fitted.values, m6$fitted.values, m3$residuals, m4$residuals, m5$residuals, m6$residuals )) %>% 
                rename(Freq = X1, ExpectedMod3 = X2, ExpectedMod4 = X3, ExpectedMod5 = X4, ExpectedMod6 = X5, 
                       Resid3 = X6, Resid4 = X7, Resid5 = X8, Resid6 = X9) %>% mutate(Component3 = (Resid3^2)/ExpectedMod3,
                                                                                      Component4 = (Resid4^2)/ExpectedMod4,
                                                                                      Component5 = (Resid5^2)/ExpectedMod5,
                                                                                      Component6 = (Resid6^2)/ExpectedMod6)   %>%
                                                                mutate(Component3 = ifelse(Resid3 < 0, -Component3 , Component3),
                                                                       Component4 = ifelse(Resid4 < 0, -Component4 , Component4),
                                                                       Component5 = ifelse(Resid5 < 0, -Component5 , Component5),
                                                                       Component6 = ifelse(Resid6 < 0, -Component6 , Component6))
rownames(Expected) = rownames(All_Final_Data_Cleaned2)
Expected = round(Expected, 4) %>% arrange(Component6)
write.csv(Expected, "ExpectedFarmersMarkets.csv")
```

```{r}
head(Expected)
```
The data has been sorted in descending terms of the "negative" chi squared contributions for model 6, which has the lowest AIC.  Models 3 and 4 use the PCA components and the model has strange behavior when handling outliers.  Three major outliers were identified, Los Angeles County, CA and Cook County, IL have very large populations, and Shannon County, SD has a very high Native American population.  Thus the PCA models should be interpreted with caution, especially when dealing with counties with exetreme observations.  

Queens.County.NY, Richmond.County.NY, and Arapahoe.County.CO are the three leading candidates that show promise for holding more farmers markets.  Queens has only 19 farmers markets when the models 5 and 6 predict anywhere from about 46 farmers markets.  Arapahoe County, CO, containing portions of suburban Denver, has only 4 markets where the models predict somewhere between 9 and 14.



